JP2017051125A - Growth diagnostic method, growth diagnostic apparatus, data measurement device, growth diagnostic program, growth assisting method, growth assisting device, and growth assisting program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a growth diagnostic apparatus that diagnoses the growth of a vascular plant without using an expensive device.SOLUTION: A growth diagnostic apparatus comprises: internal information measurement systems (1a, 1b, 11) which measure data on internal information showing a growth state of a vascular plant 2a; environmental factor measurement systems (12 to 15, 51a) which measure fluctuation data on an environmental factor affecting the growth state of the vascular plant 2a; and an arithmetic control device 30 which calculates a correlation coefficient between data on the internal information and fluctuation data on the environmental factor. The growth state of the vascular plant 2a is diagnosed by a value of the correlation coefficient.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a vascular plant growth diagnostic method, a growth diagnostic device, a data measurement device, a growth diagnostic program, and a vascular plant growth assistance method, a growth assistance device, a data measurement device, and a growth assistance program based on these. In particular, the present invention relates to a technique suitable for predicting yield and sugar content after growth of vascular plants.

  The inventor has developed a technique relating to a soundness evaluation method for determining whether or not the embolism of a vascular plant has reached a level that cannot be repaired by measuring acoustic emission (AE) generated by cavitation before and after fluctuation of water stress. Have already been proposed (see Patent Document 1). However, in the measurement of AE caused by cavitation, since the energy of AE is usually weak, various problems relating to noise have occurred.

  Therefore, the present inventor has arranged a plurality of AE sensors close to the outer surface of the axis of the vascular plant, and by optimizing the distance between these AE sensors, removes AE caused by noise, An improved method for evaluating the degree of soundness of a vascular plant that can detect only AE caused by cavitation has already been proposed (see Patent Document 2).

  However, even if the method described in Patent Document 2 is used, the environment outside the plant such as temperature, humidity, sunshine, and soil moisture greatly fluctuates under the actual farming conditions such as house cultivation and outdoor cultivation. If the influence of these environmental fluctuations is not taken into account, there is a problem that it is difficult to accurately predict the growth diagnosis, yield and sugar content of the cultivated plant even if AE caused by cavitation is measured.

Japanese Patent No. 5413783 JP-A-2015-87214

  In view of the above problems, the present invention provides a growth diagnosis method, a growth diagnosis device, a data measurement device, and a growth diagnosis program for diagnosing the growth of vascular plants without using an expensive device, and further, these growth diagnosis methods It is an object of the present invention to provide a growth assisting method, a growth assisting device, a data measuring device, and a growth assisting program for achieving an improvement in the yield and quality of vascular plant crops.

  In order to achieve the above object, one aspect of the growth diagnosis method of the present invention includes (1) a step of measuring internal information data indicating the growth state of a vascular plant, and (2) measuring internal information data. Measuring the fluctuation data of environmental factors that influence the growth state in a time series overlapping with the timing to perform, and (3) calculating the correlation coefficient between the internal information data and the fluctuation data of the environmental factors. In addition, the gist is to diagnose the growth state based on the value of the correlation coefficient.

  One aspect of the growth diagnostic apparatus of the present invention includes (1) an internal information measurement system for measuring internal information data indicating the growth state of a vascular plant, and (2) fluctuation of environmental factors affecting the growth state. An environmental factor measurement system that measures data; and (3) an arithmetic and control unit that calculates a correlation coefficient between internal information data and environmental factor variation data. The growth state is diagnosed by the value of the correlation coefficient. This is the gist.

  One aspect of the data measuring apparatus of the present invention is a data measuring apparatus that transmits data to an arithmetic and control unit of a computer system. (1) From an internal information measuring sensor attached to a vascular plant, a vascular bundle From the internal information measuring device that acquires the internal information data indicating the growth state of the plant, and (2) the environmental factor measurement sensor that measures the external environment of the vascular plant, the fluctuation data of the environmental factor that affects the growth state And (3) the output of the data measuring device is transmitted to the arithmetic control device, so that the arithmetic control device correlates the internal information data and the environmental factor variation data. The gist is to diagnose the growth state based on the value of the correlation coefficient.

  Also, one aspect according to the growth diagnostic program of the present invention includes (1) a command for acquiring internal information data indicating the growth state of a vascular plant, and (2) a time series overlapping with the timing for measuring the internal information data. (3) Calculate the correlation coefficient between the internal information data and the environmental factor fluctuation data, and calculate the correlation coefficient between the internal factor data and the environmental factor fluctuation data. The gist of the invention is to cause the arithmetic control device of the computer to execute arithmetic control processing by a series of instructions including a command for diagnosing the growth state of the computer.

  One aspect of the growth assisting method of the present invention includes (1) a step of measuring data of internal information indicating the growth state of a vascular plant, and (2) a time series overlapping with the timing of measuring the data of internal information. Measuring the variation data of environmental factors affecting the growth state, (3) calculating the correlation coefficient between the internal information data and the environmental factor variation data, and (4) the calculated correlation coefficient And a step of adjusting the value of the environmental factor by an adjustment amount determined by the value of

  Also, one aspect of the growth assisting apparatus of the present invention includes (1) an internal information measurement system for measuring internal information data indicating the growth state of a vascular plant, and (2) fluctuations in environmental factors affecting the growth state. Environmental factor measurement system that measures data, (3) Regulator that adjusts the value of environmental factor, (4) The correlation coefficient between internal information data and environmental factor fluctuation data is calculated, and the correlation coefficient And an arithmetic control device that outputs a command for controlling the adjusting device with an adjustment amount determined by a value, and the gist is to assist the growth of vascular plants.

  One aspect of the data measuring apparatus of the present invention is a data measuring apparatus that transmits data to an arithmetic and control unit of a computer system. (1) From an internal information measuring sensor attached to a vascular plant, a vascular bundle From the internal information measuring device that acquires the internal information data indicating the growth state of the plant, and (2) the environmental factor measurement sensor that measures the external environment of the vascular plant, the fluctuation data of the environmental factor that affects the growth state (3) the output of the data measuring device is transmitted to the arithmetic control device of the computer system, so that the arithmetic control device transmits the internal information data and the environmental factor fluctuation data. The gist is to calculate the correlation coefficient and (4) adjust the value of the environmental factor by an adjustment amount determined by the value of the correlation coefficient to assist the growth of the vascular plant.

  One aspect of the growth assistance program of the present invention includes (1) a command for acquiring internal information data indicating the growth state of a vascular plant, and (2) a time series overlapping with the timing for measuring the internal information data. In order to obtain the fluctuation data of the environmental factors that affect the growth state, (3) the instruction to calculate the correlation coefficient between the internal information data and the fluctuation data of the environmental factors, and (4) the calculated correlation coefficient The gist is to cause the computer control device to execute the processing of the arithmetic control by a series of commands including a command to assist the growth of the vascular plant by adjusting the value of the environmental factor by an adjustment amount determined by the value of To do.

  According to the present invention, a growth diagnosis method, a growth diagnosis device, a data measurement device, and a growth diagnosis program for diagnosing the growth of vascular plants without using an expensive device are provided. As described above, it is possible to provide a growth assisting method, a growth assisting device, a data measuring device, and a growth assisting program for improving the yield and quality of a vascular plant crop.

It is a block diagram explaining the outline of the growth diagnostic device concerning a 1st embodiment typically. It is a block diagram which illustrates typically the composition of an arithmetic and control unit. It is a flowchart explaining the growth diagnostic method which concerns on 1st Embodiment. It is a schematic diagram explaining the method of producing the data sequence for correlation coefficient calculation. It is a graph which shows the locus | trajectory of the data of AE generation | occurrence | production between the cultivation days 80 days and 170 days. It is a graph which shows the locus | trajectory of the data row | line | column of the number of AE generation | occurrence | production between the cultivation days from 80 days. Fig.7 (a) is a graph which shows the locus | trajectory of the temperature data sequence between the cultivation days 80 days and 120 days, and FIG.7 (b) is the solar radiation amount data sequence between the cultivation days 80 days and 170 days. It is a graph which shows the locus | trajectory. FIG. 8 (a) is a graph showing the trajectory of the change amount of the temperature data sequence between 80 and 120 days of cultivation, and FIG. 8 (b) shows solar radiation between 80 and 120 days of cultivation. It is a graph which shows the locus | trajectory of the variation | change_quantity of quantity data sequence. The behavior of the temperature correlation coefficient obtained from the variation of the temperature data sequence shown in FIG. 8A and the behavior of the light correlation coefficient obtained from the variation of the solar radiation data sequence shown in FIG. FIG. It is a graph which shows the behavior of the correlation coefficient of the temperature in the whole cultivation period of winter, and the behavior of the correlation coefficient of light. It is a graph which shows the behavior of the correlation coefficient of the temperature in the whole cultivation period of summer, and the behavior of the correlation coefficient of light. FIG. 12 (a) is a graph showing the correlation of the cumulative value of the correlation coefficient between the tomato yield and the temperature during the entire cultivation period in the control plot and fuzzy plot cultivated in the example, divided into summer and winter seasons. FIG. 12 (b) shows the correlation between the average sugar content of each tomato in the control plot and the fuzzy plot cultivated in the examples and the cumulative value of the correlation coefficient of light in the entire cultivation period divided into the summer and winter seasons. FIG. FIG. 13 (a) shows the light phase in the whole cultivation period from the double value of the cumulative value of the correlation coefficient between the tomato yield and the temperature in the whole cultivation period in the control group and the fuzzy group cultivated in the examples. It is a graph which shows the correlation of the value which subtracted the cumulative value of the number of relations divided into summer and winter, and FIG.13 (b) is the average sugar content of each tomato of the control plot and fuzzy plot cultivated in the Example. Graph showing the correlation of the value obtained by subtracting the cumulative value of the correlation coefficient of temperature in the whole cultivation period from the 1.5 times the cumulative value of the correlation coefficient of light in the whole cultivation period and the summer and winter It is. It is a graph which shows the correlation of the yield value of each tomato of the control plot and the fuzzy plot cultivated in the Examples, and the cumulative value of the correlation coefficient of humidity over the entire cultivation period, divided into summer and winter. It is a schematic diagram explaining the method of producing the data row | line of the change rate of drought stress and the number of AE generation | occurrence | production, when the irrigation conditions are small amount multi-irrigation (the 1). It is a schematic diagram explaining the method of producing the data row | line of the change rate of drought stress and the number of AE generation | occurrence | production, when the irrigation conditions are small amount multi-irrigation (the 2). FIG. 19A is a graph showing the behavior of the correlation coefficient of irrigation calculated at time width tw = 1 day in the control group, and FIG. 19B is the irrigation calculated at time width tw = 1 day. It is a graph which shows the behavior in the fuzzy section of the correlation coefficient. FIG. 20A is a graph showing the relationship between the cumulative value ΣCD of the irrigation correlation coefficient and the yield of tomato fruit, and FIG. 20B is the graph of FIG. It is a graph which shows the relationship of the sugar content of a fruit. It is a flowchart explaining the growth assistance process which concerns on 1st Embodiment. It is a table | surface which shows the adjustment process in case each of the correlation coefficient of light and the correlation coefficient of temperature is less than a threshold value. It is a graph which shows the relationship between the sum total of the watering time of 1 day, and the correlation coefficient of the watering of the measured day. FIG. 22A is a graph showing the relationship between the correlation coefficient of irrigation and the average value of the variation ΔCT of the correlation coefficient of temperature, and FIG. It is a graph which shows the relationship of the average value of variation | change_quantity (DELTA) CL of relationship number. It is a block diagram explaining typically the outline of the growth diagnostic device concerning a 2nd embodiment. It is a graph which restrict | limits the behavior of the correlation coefficient of the temperature in the whole cultivation period of the Example of an Example, and the behavior of the correlation coefficient of light to data only when there is an amount of solar radiation of 20 W / m < ² > or more. It is a block diagram explaining typically an outline of a growth diagnostic device concerning a 3rd embodiment. It is a block diagram which illustrates typically the composition of an arithmetic and control unit. It is a schematic diagram explaining the calculation method of the correlation coefficient using several internal information by the arithmetic and control apparatus. It is typical sectional drawing explaining the schematic structure of a dual electret sensor. FIG. 29A is a plan view for explaining a state before the dual electret sensor is folded, and FIG. 29B is a plan view for explaining a state after the dual electret sensor is folded. FIG. 30A is a plan view of a receiving element composed of a folded film electret sensor, and FIG. 30B is a plan view of a transmitting element composed of a folded film electret sensor. 30 (c) is a plan view showing a state where the dual electret sensor is assembled by stacking the receiving element of FIG. 30 (a) and the transmitting element of FIG. 30 (b). FIG. 30D is a cross-sectional view corresponding to FIG. It is a typical bird's-eye view (perspective view) explaining the example which contacts the dual electret sensor which can be deform | transformed flexibly according to the shape of a to-be-measured sample. It is a graph which shows the behavior of a peak intensity and a gravity center frequency when measuring the hardness of the stem of a vascular plant using a dual electret sensor. It is a graph which shows the behavior of the number of AE generation | occurrence | production, and the coefficient c when measuring the hardness of the stem of a vascular plant using a dual electret sensor. It is a flowchart explaining the growth diagnostic method which concerns on 3rd Embodiment. It is a block diagram explaining typically the outline of the growth diagnostic device concerning one modification. It is a block diagram which illustrates typically the outline of the growth diagnostic apparatus concerning other modifications. It is a block diagram which illustrates typically the outline of the growth diagnostic apparatus concerning other modifications.

  The present inventor measures the AE of a vascular plant using the methods of Patent Documents 1 and 2, so that a vascular plant such as a cherry tomato is used even in an environment where temperature control is performed such as a constant temperature room. It was found that when the irrigation conditions in the cultivation of potato were varied, there was a correlation between the rate of change in the occurrence frequency of AE caused by cavitation before and after irrigation, and the yield and sugar content of fruits.

  First to third embodiments of the present invention based on the above knowledge will be described below with reference to the drawings. In the drawings, the same or similar parts are denoted by the same or similar reference numerals. However, it should be noted that the drawings are schematic, and the relationship between the thickness and the planar dimensions, the ratio of the thickness of each layer, and the like are different from the actual ones. Therefore, specific thicknesses and dimensions should be determined in consideration of the following description. Moreover, it is a matter of course that portions having different dimensional relationships and ratios are included between the drawings.

  The first to third embodiments exemplify apparatuses and methods for embodying the technical idea of the present invention, and the technical idea of the present invention is the material, shape, The structure and arrangement are not specified as follows. The technical idea of the present invention can be variously modified within the technical scope defined by the claims described in the claims. Furthermore, the directions of “left and right” and “up and down” in the following description are merely definitions for convenience of description, and do not limit the technical idea of the present invention. Thus, for example, if the paper is rotated 90 degrees, “left and right” and “up and down” are read interchangeably, and if the paper is rotated 180 degrees, “left” becomes “right” and “right” becomes “left”. Of course.

--First Embodiment--
<Growth diagnostic equipment for vascular plants>
As shown in the block diagram of FIG. 1, the growth diagnostic apparatus according to the first embodiment is an internal information measurement system (1a, 1b, 11) that measures internal information data indicating the growth state of the vascular plant 2a. And an environmental factor measurement system (12-15, 51a) that measures fluctuation data of environmental factors that influence the growth state of the vascular plant 2a, and calculates a correlation coefficient between the internal information data and the fluctuation data of the environmental factors. A computer system including the arithmetic and control unit 30. The growth diagnostic apparatus diagnoses the growth state of the vascular plant 2a based on the calculated correlation coefficient value.

  A part of the internal information measurement system (1a, 1b, 11) and the environmental factor measurement system (12-15, 51a) are used to transmit internal information from the internal information measurement sensor (1a, 1b) attached to the vascular plant 2a. Environmental factor measurement control for acquiring fluctuation data of environmental factors affecting the growth state of the vascular plant 2a from the internal information measuring device 11 for acquiring data and the environmental factor measuring sensors (12 to 15) for measuring the external environment. The unit 51a is configured to be portable by the data measuring device 7a, and can be easily placed on the growing site of the vascular plants 2a, 2b such as the greenhouse 10.

  The data measuring device 7a further includes a data bus 72. The internal information data input to the internal information measuring device 11 and the environmental factor variation data input to the environmental factor measurement control unit 51 a are stored in the temporary storage device 71 via the data bus 72. The wireless unit 73 transmits the data stored in the temporary storage device 71 to the arithmetic control device 30 via the antenna 83. The transmission of the internal information data and the environmental factor fluctuation data from the data measuring device 7a may be executed sequentially every sampling, or after being accumulated in the temporary storage device 71 without being transmitted for a certain period of time. The process of sending collectively may be performed periodically.

  In addition, although two vascular plants 2a and 2b are shown in FIG. 1, it is only a schematic illustration. There are a large number of vascular plants of several tens to several hundreds in the actual greenhouse 10 of the farmhouse. Similarly, in FIG. 1, for the sake of convenience, the case where there is only one data measuring device 7 a is schematically illustrated. Practically, it is preferable to install a plurality of portable data measuring devices in the greenhouse 10 at arbitrary locations. Even in the same greenhouse 10, environmental factors may vary greatly depending on the location in the greenhouse 10, so the growth state of vascular plants may depend on the location in the greenhouse 10. Because. In addition, since environmental factor values may vary greatly depending on the location in the greenhouse 10, even if the correlation coefficient is calculated using measured values of environmental factors at a specific point, vascular plants in other locations This is because it is necessary to consider the possibility that the growth status of the child cannot be properly diagnosed.

  Measuring the environmental factors around the vascular plant (hereinafter referred to as “sampling plant”) to which an internal information measuring device such as AE measurement is attached may more accurately diagnose the growth state of the target sampling plant. it can. An actual farmer selects a plurality of sampling plants in a plurality of locations in the greenhouse 10 from among a large number of vascular plants in practice. The arrangement of the pyranometer 12, the thermometer 13, the hygrometer 14, etc. in FIG. 1 is merely a schematic illustration that simplifies the drawing. These arrangements are practically set in the vicinity of each sampling plant and grouped for each sampling plant. By preparing a plurality of data measuring devices, a portable data measuring device can be individually assigned to the sampling plants for each of the sampling plants selected at a plurality of locations in the greenhouse 10.

  Although not shown in the figure, by using a plurality of data measuring devices, individual internal information data of each sampling plant and environmental factor variation data corresponding to the individual internal information are converted into the greenhouse 10. It is possible to measure independently of each other at a plurality of locations. Internal information data and environmental factor fluctuation data are transmitted in parallel from the respective antennas of a plurality of data measuring devices arranged at a plurality of locations in the greenhouse 10 to the arithmetic and measuring device 30 shown in FIG. The

  The AE as the internal information is measured by the first AE sensor 1a and the second AE sensor 1b attached to the shaft portion of the vascular plant 2a and the internal information measuring device 11 connected to the first AE sensor 1a and the second AE sensor 1b. The first AE sensor 1a and the second AE sensor 1b constitute an “internal information measurement sensor” of the internal information measurement system (1a, 1b, 11).

  In addition, the growth diagnosis apparatus includes a pyranometer 12 that measures the intensity of light irradiated to the vascular plant 2a, a thermometer 13 that measures the temperature outside the vascular plant 2a, and a humidity outside the vascular plant 2a. The hygrometer 14 to measure and the soil moisture sensor 15 which measures the soil moisture of the vascular plant 2a are provided. Each of the pyrometer 12, the thermometer 13, the hygrometer 14, and the soil moisture sensor 15 constitutes an “environmental factor measurement sensor” of the environmental factor measurement system (12-15, 51a) of the first embodiment.

  Vascular plants include most grasses and trees. The vascular plant 2a shown in FIG. 1 is, for example, a tomato, and a plurality of the vascular plants 2a are placed and cultivated on the cultivation bed 4 installed in the greenhouse 10. FIG. 1 illustrates a state in which two vascular plant (tomato) 2a and 2b strains are fixed in the nonwoven fabric pots 3a and 3b, respectively. Although illustration is omitted, actually, a plurality of tomato strains are arranged toward the left and right ends in FIG. Also, a plurality of rows of tomato stocks are repeatedly arranged and planted in the depth direction, which is the direction when the paper surface in FIG. 1 is viewed from the front.

  Among a plurality of tomato strains, some tomato strains are selected as targets for the growth diagnosis method according to the present invention, and an AE sensor is attached. In 1st Embodiment, the 1st AE sensor 1a and the 2nd AE sensor 1b are provided in the surface of the stem of the left tomato 2a among the two tomatoes 2a and 2b in FIG. Yes. The AE sensor can be provided on any of the main axis (stem), side axis, or leaf (leaf vein, petiole) of tomato (hereinafter collectively referred to as “axis”). The first AE sensor 1a and the second AE sensor 1b are vibration sensors such as a piezoelectric type, an electret type, and an electrostatic type, and can detect vibrations in an ultrasonic region of, for example, 20 kHz to 1000 kHz (desirably 50 to 200 kHz).

  The first AE sensor 1a is disposed closest to the cavitation occurrence location, and the first AE sensor 1a alone measures the occurrence of AE caused by cavitation as internal information. On the other hand, the second AE sensor 1b can detect the same internal information as that detected by the first AE sensor 1a but caused by noise such as noise and stem vibration, but is caused by cavitation detected by the first AE sensor 1a. The internal information is attached in a relative positional relationship that cannot be detected. The internal information detected by the first AE sensor 1a and the second AE sensor 1b is input to the internal information measuring device 11.

  The internal information measuring device 11 removes internal information caused by noise from each internal information signal input from the first AE sensor 1a and the second AE sensor 1b, and detects only the internal information caused by cavitation. The obtained AE is input to the arithmetic and control unit 30. The inputted internal information data is stored in the data storage device 42 via the arithmetic control device 30. The AE sampling interval of the internal information measuring device 11 is appropriately determined according to the sampling rate preset in the internal information measuring device 11.

The pyranometer 12 is provided in the upper part of the greenhouse 10, and measures the amount of solar radiation irradiated to the tomato 2a with time as fluctuation data of environmental factors. “Solar radiation” is one of the light factors, and is measured empirically in the range of about 0 to 0.9 kW / m ² throughout the year. The measured solar radiation amount data is input to the arithmetic and control unit 30 via the environmental factor measurement control unit 51a. As the light factor, in addition to the amount of solar radiation, for example, the illuminance of sunlight can be adopted, and it is empirically measured in the range of about 0 to 100,000 Lx. In addition, the photon density of sunlight can be adopted as an optical factor.

  The thermometer 13 and the hygrometer 14 are provided in the inside of the greenhouse 10 and measure the temperature and humidity outside the tomato 2a with time as variation data of environmental factors. “Temperature” is empirically measured in the range of −10 ° C. to 60 ° C. throughout the year. “Humidity” is relative humidity and is measured in a range of about 0 to 100% throughout the year. The measured temperature and humidity data are input to the arithmetic and control unit 30 via the environmental factor measurement control unit 51a. The thermometer 13 and the hygrometer 14 may be provided separately or may be provided integrally.

  The soil moisture sensor 15 is provided by being inserted into the soil in the nonwoven fabric pot 3a of the tomato 2a, and measures soil moisture over time as variation data of environmental factors. "Soil moisture" is one of the rhizosphere moisture factors that are environmental factors, and is measured in the range of air drying to saturation throughout the year. Soil moisture may be represented by volumetric water content, or may be represented by a predicted value calculated from irrigation conditions such as irrigation time and irrigation time. The measured soil moisture data is input to the arithmetic and control unit 30 via the environmental factor measurement control unit 51a.

  As the rhizosphere moisture factor, in addition to soil moisture, for example, the dielectric constant or electric conductivity of soil or nutrient solution can be employed. Dielectric constant and electrical conductivity can be effectively used as rhizosphere moisture factors in hydroponics etc. without using soil. As other rhizosphere moisture factors, irrigation conditions and the relative humidity of the rhizosphere space can be adopted and can be used effectively in hydroponics. That is, the present invention can be applied regardless of the cultivation state such as culture medium, no culture medium, and hydroponics.

  Data on environmental factors used to calculate correlation coefficients for solar radiation, temperature, humidity, and soil moisture are all relative amounts, that is, changes with time (difference values), as in the case of the number of AE occurrences. The relative values used can be used. Moreover, it is not necessary to calibrate the measured value to obtain an exact value. Data relating to each environmental factor is measured as a digital value of 1 to 16 bits (preferably 3 to 8 bits).

  In addition, the sampling interval of the fluctuation data of the environmental factors of solar radiation, temperature, humidity and soil moisture is appropriately determined according to the sampling rate set in advance in the device for measuring each, but for each environmental factor each other May be different. The environmental factor variation data, which is at least one of the sampled amount of solar radiation, temperature, humidity, and soil moisture, is stored in the data storage device 42 via the arithmetic control device 30.

  The arithmetic control device 30 can be realized by, for example, a central processing unit (CPU) or a processor of a computer, and an input device 41 and a data storage device 42 are connected to the arithmetic control device 30. The arithmetic control device 30 is configured to be able to access data stored in the data storage device 42. A wireless unit 74 having a transmission / reception antenna 84 is connected to the arithmetic control device 30.

  As shown in the block diagram of FIG. 2, the arithmetic and control unit 30 creates an AE data string creation unit that creates a data string of the number of occurrences of AE as internal information with a certain time width necessary for calculating a correlation coefficient. 301. The arithmetic and control unit 30 also includes a solar radiation data string creation unit 302 that creates a solar radiation data string in a certain time width necessary for calculating the correlation coefficient, and a temperature data string that creates a temperature data string. It includes at least one of a creation unit 303, a humidity data sequence creation unit 304 that creates a humidity data sequence, and a soil moisture data sequence creation unit 305 that creates a soil moisture data sequence.

  The function of the AE data sequence creation unit 301 is included in the internal information measurement device 11 of the data measurement device 7a, and the solar radiation data sequence creation unit 302, the temperature data sequence creation unit 303, the humidity data sequence creation unit 304, and the soil moisture data. Each function of the column creation unit 305 may be included in the environmental factor measurement control unit 51a, and the created data row may be stored in the temporary storage device 71. Data stored in the temporary storage device 71 is transmitted to the arithmetic control device 30 via the wireless unit 73, the antenna 83, the antenna 84, and the wireless unit 74.

  The arithmetic and control unit 30 also includes a correlation coefficient calculation unit 307 that calculates a correlation coefficient between the number of AE occurrences in a certain time width and at least one of the amount of solar radiation, temperature, humidity, and soil moisture. The arithmetic and control unit 30 also includes a correlation coefficient determination unit 308 that determines whether or not the calculated correlation coefficient is equal to or greater than a certain threshold value. The correlation coefficient determination unit 308 is used in a growth assisting device described later.

<Diagnosis method for growth of vascular plants>
Next, the growth diagnosis method according to the first embodiment will be described. First, in step S1 of the flowchart of FIG. 3, the arithmetic and control unit 30 selects only AEs caused by cavitation as internal information via the internal information measuring unit 11, and measures the data of the number of occurrences of the selected AEs.

  As a method for detecting only AE caused by cavitation as internal information, for example, the method described in Patent Document 2 can be used. Specifically, for example, in the internal information measuring apparatus 11, after AD conversion of each internal information signal input from the first AE sensor 1a and the second AE sensor 1b as the internal information measuring sensor, a band such as an IIR filter is used. The signal generated through the pass filter is output. As the bandpass filter, for example, a Butterworth sixth-order bandpass filter circuit that extracts a signal having a frequency of 20 to 100 kHz can be used.

  Then, in the two output signals of the output signal of the first AE sensor 1a and the output signal of the second AE sensor 1b, when an unexpected signal exceeding a predetermined threshold is detected, the amplitudes of the two output signals are compared, If one is larger than the other by a certain value or more, it is determined that valid internal information has been detected, and the detection time of the internal information detected together with the magnitude of the output signal is input to the arithmetic and control unit 30. As a value used for comparing the amplitudes of the output signals, about 12 dB can be used. The magnitude and detection time of the output signal input to the arithmetic control device 30 are stored in the data storage device 42.

  On the other hand, if the difference in magnitude between the amplitudes of the two output signals is less than a certain value, it is determined that valid internal information has not been detected, and the process returns to the above-described sudden signal detection process. In this way, the time-dependent data of the number of AE occurrence necessary for calculating the correlation coefficient is accumulated in the arithmetic and control unit 30 as internal information data. As the measured value of the number of AEs used for calculating the correlation coefficient, in addition to the actual measured value, the difference value between two data adjacent to the time series is sequentially calculated and the change amount or relative value is used. May be. Since the correlation coefficient can be calculated, it is not necessary to calibrate the measured value to obtain an exact value.

  Next, in step S2, the wireless unit 74 of the arithmetic and control unit 30 receives the variation data of the environmental factor that is at least one of light, temperature, humidity and rhizosphere moisture from the data measuring device 7a via the antenna 84. To do. For example, the arithmetic and control unit 30 receives the solar radiation amount data measured with time by the data measuring device 7 a via the solar radiation meter 12 and accumulates it in the data storage device 42. Alternatively, the arithmetic and control unit 30 receives the temperature data measured over time by the data measuring device 7 a via the thermometer 13 and accumulates it in the data storage device 42. In addition, the arithmetic and control unit 30 receives humidity data measured over time by the data measuring device 7 a via the hygrometer 14 and accumulates it in the data storage device 42. Further, the arithmetic and control unit 30 receives the soil moisture data measured over time by the data measuring device 7 a via the soil moisture sensor 15 and accumulates it in the data storage device 42. The measurement timing of the fluctuation data of the environmental factors (light, temperature, humidity, rhizosphere moisture) in step S2 and the measurement timing of the internal information in step S1 are performed so as to overlap with each other.

  Next, in step S3, the AE data string creation unit 301 of the arithmetic and control unit 30 uses the detection time of the number of AE occurrences as internal information accumulated in the data storage device 42 or the number of AE occurrences at a certain sampling interval s. As shown in the graph of FIG. 4A, a data group of the number of AE occurrences is created. The sampling interval s is about 1 second to about 1 day, and preferably about 1 minute to 1 hour. 4A, the heights of the discrete data a1, a2,... A6,... A11,... Recorded at times t1 to t31,. Represents.

  Next, as shown in the graph of FIG. 4B, the AE data string creation unit 301 converts the discrete data of the measured values shown in FIG. 4A into measured values for each fixed time interval ti. A data string for correcting and calculating the correlation coefficient is created. The data string may be created by calculating discrete data remaining after thinning out the discrete data a1, a2, ... a6, ... a11, ... of the measured values, or included in a fixed time interval ti. An average value of a plurality of data may be obtained every time interval ti.

  In addition, the number of AE occurrences as internal information that can be measured as compared with the time interval ti is reduced, or conversely, it is suddenly increased and the amount of data fluctuation becomes extremely large. In this case, the constant time interval ti can be set to a value that suppresses the fluctuation amount within a range of about 1 hour to about 1 month, preferably about 6 hours to 24 hours. In addition to the setting of the time interval ti, the measured data may be subjected to moving average processing such as a simple moving average, a load moving average, and an exponential moving average to suppress the fluctuation amount.

  As shown in FIG. 4B, the time interval ti is set by accumulating a predetermined number of sampling intervals s. In other words, the time interval ti is the length of time between the elements of the data string, and is a unit time for dividing all the lengths of the target period (growth diagnosis period) for calculating the correlation coefficient into the same time. ing.

  In FIG. 4B, in the section of t1 ≦ t <t6, the first discrete data a1 measured at the beginning of the section is selected as a data string representing this section, and other discrete data a2 to a5 are illustrated as being thinned. In the next section of t6 ≦ t <t11, the leading discrete data a6 is selected as a data string representing this section, and also in the next section of t11 ≦ t <t16, the leading discrete data a11 is selected. However, the state selected as a data string representing this section is illustrated.

Next, in step S4, the solar radiation amount data string creation unit 302 of the arithmetic and control unit 30 generates a data group for calculating the correlation coefficient using the data for each detection time of the solar radiation amount accumulated in the data storage device 42. create. In or step S4, the amount of solar radiation data string creation unit 302, by using the solar radiation amount data for each predetermined sampling interval s _L, as in the case of AE incidence shown in FIG. 4 (a), the amount of solar radiation data Create a group. Then, as in the case of the AE data string shown in FIG. 4B, the discrete data of the solar radiation measurement values are converted into the measurement values for each fixed time interval ti and corrected to calculate the correlation coefficient. Create a data column for The sampling interval s _L may be the same as or different from the sampling interval s shown in FIG. 4A, but it is desirable that the common multiple of s _L and s is equal to ti.

  When the environmental factor includes temperature, the temperature data string creation unit 303 of the arithmetic and control unit 30 creates a temperature data group in the same manner as in the case of the amount of solar radiation, and from the created data group for each fixed time interval ti. Create a temperature data string. When the environmental factor includes humidity, the humidity data string creation unit 304 of the arithmetic and control unit 30 similarly creates a humidity data group, and creates a humidity data string for each fixed time interval ti from the created data group. To do. When the environmental factor includes rhizosphere moisture content, the soil moisture data string creation unit 305 of the arithmetic and control unit 30 creates a soil moisture data group in the same manner, and the soil at a certain time interval ti from the created data group. Create a moisture data string.

  Next, in step S5, the correlation coefficient calculation unit 307 of the arithmetic and control unit 30 uses the AE data string as the internal information data and the environmental factor data string to correlate each other for a certain time width tw. Calculate the number. The correlation coefficient is expressed by the following equation (1), for example, where xi is the internal information data string and yi is the environmental factor data string.

  As shown in the graph of FIG. 4C, the time width tw is set by accumulating a predetermined number of time intervals ti. The length of the time width tw is about 1 hour to about 1 month, preferably about 6 hours to 24 hours, based on empirical rules. In the case of the data sequence illustrated in FIG. 4C, for the time width tw including the interval t1 ≦ t <t16, three data sequences a1, a6, and a11 represented by circles calculate the correlation coefficient. Will be used. Also, the time width composed of the section of t16 ≦ t <t31 includes three data strings, and these three data strings have the correlation coefficient as in the case of the previous time width tw (t1 ≦ t <t16). It will be used for the calculation of. Thus, the calculation of the correlation coefficient between the internal information data represented by the equation (1) and the environmental factor variation data is performed within the same time width tw.

  For the calculation of the correlation coefficient, the internal information and the measured value of the environmental factor may be used by converting the scales such as logarithm, exponent, and power function. In calculating the correlation coefficient, the amount of change or the rate of change of each measured value for each time interval ti may be used as a data string.

(Example of growth diagnosis method using correlation coefficient CT with temperature and correlation coefficient CL with light)
Next, an example of the growth diagnosis method according to the first embodiment will be described in the case of using the greenhouse 10 of FIG. Tomatoes (variety: Momotaro Haruka, Takii seedlings) were cultivated in a greenhouse 10 having a rectangular ground of about 4 m × about 8 m shown in FIG. 1, and internal information and environmental factors were measured. Cultivation was performed in two periods, winter (late 11 to early May) and summer (late July to early November), and the correlation coefficient between internal information and environmental factors was calculated for each period.

  As cultivation conditions, 32 tomato seedlings were grown for 30 days and then planted in the greenhouse 10. The non-woven pots 3a and 3b were installed on the cultivation bed 4, and the tomato 2a and 2b strains were fixed to the upper portions of the non-woven pots 3a and 3b, and were cultivated in a medium-free condition. And each drip irrigation of the nutrient solution was performed to each strain | stump | stock using the dripper. At this time, the electric conductivity of the nutrient solution was 0.5 mS / m. Then, 32 tomato strains were divided into 2 sections each having 16 tomatoes, one being fixed to a sufficient irrigation condition that does not cause drought stress, and the other being irrigated time or irrigation by fuzzy control so that drought stress would occur. It was set as a “fuzzy ward” with varying intervals. The irrigation conditions are shown below.

Winter period Control zone: irrigation interval 30 minutes, irrigation time 15 minutes Fuzzy district: irrigation interval 30 minutes, irrigation time within 1 to 20 minutes Summer season Control zone: irrigation interval 6 minutes, irrigation time 1 minute Fuzzy district: irrigation interval 3 minutes or more ~ 120 minutes or less, watering time 1 minute

  And the vibration sensor of NEC TOKIN Corporation was attached as the 1st AE sensor 1a and the 2nd AE sensor 1b on the upper and lower sides of the stem of the same tomato 2a stock as shown in FIG. Further, the internal information measuring system (1a, 1b, 11) was configured by connecting the internal information measuring device 11 to the first AE sensor 1a and the second AE sensor 1b as internal information measuring sensors.

  Further, a temperature / humidity logger manufactured by OMRON Corporation was installed in the greenhouse 10 as a thermometer 13 and a hygrometer 14 as sensors for measuring environmental factors, as shown in FIG. Similarly, a solar radiation meter 12 as an environmental factor measurement sensor was installed in the greenhouse 10 and an environmental factor measurement system (12, 13, 14, 15) was constituted by the temperature / humidity logger and the solar radiation meter 12.

  In step S1 of FIG. 3, only the AE caused by cavitation was detected by the internal information measurement system (1a, 1b, 11), and the number of detections and the detection date / time in seconds were recorded. Then, the internal information measurement system (1a, 1b, 11) is introduced into the control group and the fuzzy group, respectively, and from the data obtained from the internal information measurement system (1a, 1b, 11), the sampling interval s = 1 second in FIG. As shown in the graph, the number of AE occurrences was calculated, and the number of AE occurrences was measured for each control group and fuzzy group.

Next, in step S2, the measured values of temperature, humidity, and solar radiation are measured simultaneously with the measurement of AE at the sampling interval s = 5 minutes by the environmental factor measurement system (12, 13, 14, 15). Recorded.
Next, in step S3, the time interval ti was set to 1 hour, and as shown in the graph of FIG. Furthermore, a difference (change amount) (Δx _i = x _i −x _i−1 ) between two consecutive values in the data string of the number of AE occurrences was calculated.

  Next, in step S4, the temperature data string is created by calculating the average value for each time interval ti at the same time interval ti as in the case of AE, as shown in the graph of FIG. . Then, as shown in the graph of FIG. 8A, the change amount ΔT of the temperature data string was calculated based on the difference between two consecutive values as in the case of AE. Further, assuming that ti = 1 hour, as shown in the graph of FIG. 7B, the solar radiation amount data string was created by calculating an average value for each time interval ti. Then, as shown in the graph of FIG. 8B, the change amount ΔL of the solar radiation amount data string was calculated based on the difference between two consecutive values as in the case of AE. Further, the humidity data string was created by calculating the average value for each time interval ti at the same time interval ti of 1 hour. Then, the amount of change ΔH in the humidity data string was calculated from the difference between two consecutive values.

  Next, in step S5, the time width tw was set to 1 day, and the correlation coefficient CT with the temperature was calculated as indicated by a locus connecting square data points with a solid line in the graph of FIG. Further, the time width tw was set to the same day, and the correlation coefficient CL with the light was calculated as indicated by the locus of connecting the data points in the shape of a perfect circle with a broken line in the graph of FIG. Although not shown, the time width tw was set to the same day, and the correlation coefficient CH with humidity was calculated.

  Generally, when the temperature and the amount of solar radiation, which are environmental factors, increase, the number of occurrences of AE as internal information data tends to increase. Further, when the humidity, which is an environmental factor, decreases, the number of AE generation tends to increase. Therefore, when the temperature and the amount of solar radiation increase, the number of AE occurrences often shows a positive correlation with the temperature and sunlight, and as shown in FIG. 9, the correlation coefficient may show a positive value. Become more.

  As can be seen from the behavior of the correlation coefficient CT between the AE generation number and the temperature and the correlation coefficient CL between the AE generation number and the light shown in FIG. 9, the correlation coefficient CT between the AE generation number and the temperature, the AE generation number, and the light intensity. The greater the correlation coefficient CL, the more the transpiration rate and the growth rate of the vascular plant 2a change according to the environment. That is, when the correlation coefficient is large, photosynthesis is more actively performed when the amount of solar radiation is strong, and the plant grows more when the temperature is high. Therefore, the larger the correlation coefficient CT between the number of AE occurrences and the temperature and the correlation coefficient CL between the number of AE generations and the light, it can be diagnosed that the plant is growing healthy.

  In the winter season, as shown in the graph of FIG. 10, as the cultivation period progresses, the correlation coefficient CT between the number of AE generation and temperature indicated by the solid line and the correlation coefficient CL between the number of AE generation and light indicated by the broken line Also showed a rising trend. In particular, it is a clear upward trend until around 120 days of cultivation, and it can be seen that photosynthesis and growth are more actively performed with the growth of the tomato strain. On the other hand, in summer, as shown in FIG. 11, from around 60 days of cultivation, the correlation coefficient CT between the number of AE occurrence and temperature indicated by a solid line and the number of AE occurrence and light indicated by a broken line are as follows. Contrary to the winter season, all showed a tendency to decrease. The changes in the correlation coefficient shown in FIGS. 10 and 11 were each made by plotting the index-weighted moving average value for 14 days.

Further, as shown in the graph of FIG. 12A, the yield increased as the cumulative value ΣCT of the correlation coefficient CT between the number of AE occurrences and the temperature increased. Further, as shown in the graph of FIG. 12B, the sugar content increased as the cumulative value ΣCL of the AE generation number and the light correlation coefficient CL increased.
In addition, as shown in FIG. 12, tomato As grown in the control zone in summer and tomato Bs grown in the fuzzy zone are tomato Bw grown in the control zone in winter and tomato Bw cultivated in the fuzzy zone. In comparison, the yield and sugar content of both strains were significantly reduced. According to the results shown in FIG. 12, when the cumulative values of the correlation coefficient CT and the correlation coefficient CL in the summer are smaller than the cumulative values of the correlation coefficient CT and the correlation coefficient CL in the winter, the tomato fruit yield and sugar content are reduced. It turns out that it falls. The cumulative values of the correlation coefficient CT and the correlation coefficient CL in the summer are small, as shown in FIG. 11, because the correlation coefficient CT and the correlation coefficient CL are small during the cultivation of tomatoes. ing.

  In addition to simply accumulating correlation coefficients as shown in FIG. 12, a plurality of correlation coefficients are each multiplied by a predetermined coefficient, and an index combined to obtain these differences is used. May be. For example, as shown in FIG. 13A, from the double value (2ΣCT) of the cumulative value of the correlation coefficient CT between the number of AE occurrences and the temperature (2ΣCT), the cumulative value (ΣCL) of the correlation coefficient CL between the number of AE generations and light is calculated. It can be seen that the subtracted value (2ΣCT−ΣCL) has a strong correlation with the yield of tomato. Further, as shown in FIG. 13B, the cumulative value of the correlation coefficient CT between the number of AE occurrences and the temperature (1.5 ΣCL) from the cumulative value of the correlation coefficient CL between the AE generations and the light (1.5ΣCL) ( It can be seen that the value obtained by subtracting (ΣCT) (1.5ΣCL−ΣCT) has a strong correlation with the average sugar content of tomato.

  Thus, by calculating the index combined as described above from the correlation coefficient CT between the number of AE generation and temperature during cultivation and the correlation coefficient CL between the number of AE generation and light, the yield and sugar content of tomato during cultivation are calculated. Can be predicted more accurately. It should be noted that a combination pattern of coefficients and differences for configuring the index is appropriately set according to an empirical rule.

  Further, as shown in FIG. 14, it can be seen that the cumulative value ΣCH of the correlation coefficient CH with humidity also has a strong correlation with the yield. Thus, in addition to the correlation coefficient CT between the AE generation number and the temperature and the correlation coefficient CL between the AE generation number and the light, the correlation coefficient CH between the AE generation number and the humidity and the correlation coefficient CS between the AE generation number and the soil moisture are also included. The growth diagnosis method can be performed by handling the correlation coefficient CT and the correlation coefficient CL in the same manner.

  However, when humidity and soil moisture are used as environmental factors, the generation of AE is promoted as the measured value decreases. Therefore, if the measured value and the change amount are used as they are as a data string, a negative correlation with AE is obtained. More to show. Therefore, for humidity and soil moisture, in order to match the polarity of the correlation coefficient CT between the number of AE generation and temperature and the correlation coefficient CL of the number of AE generation and light, the polarity of the measured value and the numerical value of the change amount are reversed. When used, the handleability in growth diagnosis is enhanced.

(Example of growth diagnosis using correlation coefficient with irrigation)
Next, a method for diagnosing growth using the correlation coefficient of AE generation as an environmental factor and the number of AE occurrences as internal information data will be described.
First, before using the correlation coefficient CD of the number of AE occurrences and irrigation, it is determined whether the irrigation condition is a small amount of multiple irrigation. For example, in the hydroponics where the root area is restricted by the nonwoven fabric pots 3a and 3b, a small amount of multiple irrigation such as irrigation multiple times per hour may be performed. When the irrigation condition is not a small amount of multi-irrigation, the correlation coefficient CD of AE occurrence and irrigation can be calculated without any problem. However, there are cases in which changes due to irrigation cannot be accurately detected, and the creation process of the soil moisture data string cannot be executed effectively.

  Also, if the irrigation interval is too short with a small amount of multiple irrigation, the number of detected AEs may be insufficient. For example, in the case of tomato cultivation, only a few AEs may be detected per hour per strain. However, even if it is difficult to detect the soil moisture measurement value and AE, it is a fact that the drought stress of the plants is alleviated by irrigation, and the fluctuation state of the drought stress due to irrigation surely exists.

  In such a situation where the number of detections is relatively small, in order to accurately capture the fluctuation of the number of AE occurrences as internal information data, the time interval ti of the data string should be set to 1 hour or more, for example. In the case of multiple irrigation, since the irrigation interval is shorter than 1 hour, considering the consistency with the time interval ti on the irrigation condition side, a method of extending the time interval ti for 1 hour or more is not preferable. Therefore, the present inventor creates a data string of the rate of change of drought stress and the number of AE occurrences by the procedure shown in the following (1) to (4), so that the data sequence of the rate of change of drought stress and the number of AE occurrences. I devised a method to find out effectively.

(1) In FIG. 15A, the time of irrigation during one hour is illustrated by the positions of five downward arrows. Depending on the positions of these five arrows, as shown in FIG. 15 (b), the drought stress is reduced between the irrigation time at one point and the middle point between the irrigation point and the next irrigation point. The period from the intermediate time to the next irrigation time is defined as the "+1" period when the drought stress increases. That is, the rate of change in drought stress is simplified to ± 1 and 1-bit information.

(2) On the other hand, in FIG. 15C, the detection time of AE detected during the same hour as shown in FIGS. 15A and 15B is 13 downward arrows. It is illustrated by the position. Next, referring to FIG. 15B, whether the detection time of 13 AEs is AE (H) when the drying stress is increasing or AE (L) when the drying stress is decreasing, respectively. Classify. In the case of the detection time of 13 AEs in FIG. 15C, there are 10 AE (H) and 3 AE (L).

(3) Next, a time interval ti larger than the actual irrigation interval is set. Then, it is created so that the change rate of the drought stress is roughly divided into two data strings of -1 time and +1 time within one time interval ti. For example, when the time interval ti is 1 hour, as shown in FIG. 15 (d), the time of -1 and the time of +1 are each divided by 30 minutes.

(4) Next, a data string of the total number ΣL of occurrences of AE (L) at the time interval ti and a data string of the total number of occurrences of AE (H) ΣH are respectively set to −1. The data sequence is created by combining the data sequence of the time of +1 and the time of +1 with the time. For example, when the time interval ti is 1 hour, it is the same as the number of AE (L) and AE (H) in the case of FIG. 15C, and the data string at the time of -1 is ΣL = 3, and the data at the time of +1 The data string is ΣH = 10.

  By repeating the steps (1) to (4) above, even in a small amount of multiple irrigation, as shown in FIG. It becomes possible to create continuously and effectively. Also, the data string of the number of AE occurrences can be continuously and effectively created as shown in FIG.

  In FIG. 16 (a), in the case of a small amount of multiple irrigation in which the irrigation interval is 30 minutes as in the winter in the example and the periodic irrigation is performed twice per hour by the procedure of (1) above. The rate of change in drought stress is illustrated. As shown in FIG. 16A, the drying stress is set so as to repeat the change of -1 and +1 every hour. In other words, although the irrigation is actually performed twice per hour, when calculating the correlation coefficient, the irrigation event is simplified by assuming that the variation of the drying stress is one time.

  In FIG. 16B, the AE detection times in the corresponding time zones are classified into AE (H) and AE (L) according to the procedures (2) to (4), and the rate of change of the drying stress is shown in FIG. The total number of occurrences of AE (L) when -1 is -1, and the total number of occurrences of AE (H) when the rate of change in drying stress is +1 The data string is illustrated. For example, the number of AE occurrences corresponding to the time α1 of −1 in FIG. 16A is the leftmost data point A1 in FIG. In addition, the number of AE occurrences corresponding to the +1 time α2 subsequent to the time α1 in FIG. 16A is the data point A2 subsequent to the data point A1 in FIG.

  In addition, FIG. 16C illustrates a case where the created data string of the number of AE occurrences is further converted into a data string of difference values (change amounts). The data string of the number of AE occurrences corresponding to the +1 time α2 in FIG. 16A is the data point “A2-A1” in FIG.

  And the correlation coefficient CD of AE generation | occurrence | production and irrigation is calculated using the created data sequence. As shown in the graph of FIG. 17A, the correlation coefficient CD of the number of AE occurrences and irrigation fluctuates in the vicinity of 0 in the control area where the irrigation interval is kept constant so that there is no drought stress. The amount of fluctuation is small. Moreover, the time when the correlation coefficient CD of the AE occurrence number and the irrigation is negative is a period of about 44% (71 days out of 162 days) in the whole cultivation period, and from the fuzzy ward shown in FIG. long.

  On the other hand, as shown in the graph of FIG. 17 (b), in the fuzzy group, the correlation coefficient CD of AE occurrence and irrigation greatly fluctuates compared to the control group, and is about 90% (162 days) during the entire cultivation period. The period of 145 days) is positive and the period of positive values is long. That is, in the fuzzy zone, AE tends to increase with an increase in drought stress, and the value of the correlation coefficient CD is larger than that in the control zone.

  Moreover, as shown in the graph of FIG. 18A, the yield of tomato Bs cultivated in the summer fuzzy district was lower than that of tomato As cultivated in the summer control district. In addition, the yield of tomato Bw cultivated in the winter fuzzy district was lower than that of the tomato Aw cultivated in the winter control district. However, as shown in the graph of FIG. 18B, the sugar content of tomato Bs cultivated in the summer fuzzy district was higher than that of tomato As cultivated in the summer control zone. In addition, the sugar content of tomato Bw cultivated in the winter fuzzy zone was higher than that of the tomato Aw cultivated in the winter control zone. This is because the drought stress in Fuzzy District was strong. That is, as shown in FIG. 17B, it can be diagnosed that the sugar content increases if the time when the correlation coefficient CD of AE occurrence and irrigation shows a positive value is long. On the other hand, if the correlation coefficient CD of AE occurrence and irrigation shows 0 or a negative value as shown in FIG. 17A, the yield can be diagnosed.

  According to the growth diagnosis method according to the first embodiment, environmental factors (temperature, humidity, sunshine, soil moisture or irrigation) outside the vascular plant 2a and internal information (AE caused by cavitation) are simultaneously measured. Thus, it is possible to calculate the correlation coefficient between the variation data of the environmental factor and the internal information data in the same time width, and to perform the growth diagnosis of the cultivated plant more accurately than before.

  According to the growth diagnostic method according to the first embodiment, the output of an environmental factor measurement sensor such as a commercially available AE sensor or thermometer is connected to a small data measurement device 7a. By transmitting the data to a typical computer (PC) or the like and configuring the arithmetic and control unit 30 provided in the PC, it is possible to perform plant growth diagnosis at low cost without using an expensive device.

  In the growth diagnosis method according to the first embodiment, it is possible to directly use the measured values of environmental factors and internal information and the amount of change in the measured values as a data string for calculating the correlation coefficient. However, for example, the measured values of specific environmental factors and specific internal information may fluctuate under the influence of other environmental factors so that the electrical conductivity of the soil is affected by temperature. In such a case, it is possible to improve the usefulness as a data string for calculating the correlation coefficient by calibrating the measured value in consideration of the influence due to other environmental factors.

<Growth assistance device for vascular plants>
As described in the embodiment, the growth of the vascular plant 2a can be diagnosed by using the correlation coefficient between the internal information data and the fluctuation data of the specific environmental factor. Alternatively, the growth of the vascular plant 2a can be diagnosed by using an index comprising a combination of internal information data and a plurality of correlation coefficients. Therefore, if the correlation coefficient data is fed back and a predetermined treatment is applied to the environmental factors of the vascular plant 2a to assist the growth, the growth according to the first embodiment, which enables high yield and high quality cultivation. A growth support method based on a diagnostic method can be configured. First, the structure of the growth assistance apparatus of the vascular plant 2a which concerns on 1st Embodiment is demonstrated.

  In addition to the structure of the growth diagnostic apparatus shown in FIG. 1, the growth assisting apparatus according to the first embodiment is a solar radiation amount adjusting device 21 that adjusts the solar radiation amount provided in the greenhouse 10, and sprays water and mist. Spraying device 22 for ventilating, ventilating device 23 for discharging the air in the greenhouse 10, an air conditioner 24 for adjusting the temperature inside the greenhouse 10, an irrigation device 25 for supplying water to the rhizosphere of the tomato which is the vascular plant 2a, etc. And an adjustment device for adjusting the value of the environmental factor data.

The solar radiation amount adjusting device 21 can be constituted by, for example, a light-shielding curtain, and is provided on the roof of the greenhouse 10 or the ceiling inside. It is possible to control not only the amount of solar radiation but also the temperature by adjusting the opening of the light shielding curtain.
For example, the spraying device 22 is provided on the upper side of the tomato inside the greenhouse 10 and can spray water or mist to control the internal temperature.
The ventilator 23 can be configured by, for example, a ventilator, and the internal temperature can be controlled by switching on / off the ventilator and adjusting the rotation speed.
The air conditioner 24 can be composed of, for example, an air conditioner, and the internal temperature can be controlled by blowing air by switching between hot air / cold air. If the air conditioner 24 has a dehumidifying function, the internal humidity can be controlled.

  The irrigation device 25 is a water supply device having a water supply pipe connected to a water supply tank (not shown) via a pipe, for example, and sucks water from the liquid supply tank by a pump 36 and sends it out to the water supply pipe. Water is supplied to each of the non-woven pots 3a and 3b. By adjusting the water absorption operation of the pump 36, soil moisture can be controlled.

  As shown in FIG. 1, the growth assisting apparatus according to the first embodiment includes a solar radiation amount control unit 32, a temperature control unit 33, a humidity control unit 34, a soil moisture control unit 35, and a receiving antenna 85. A work control device 8 a having a wireless unit 73 is provided. The work control device 8a receives information on commands from the arithmetic control device 30 for each adjustment device, and transmits the information to each adjustment device.

  The solar radiation amount adjusting device 21 is connected to the solar radiation amount control unit 32 of the work control device 8a that adjusts the opening degree of the light shielding curtain. The solar radiation amount control unit 32 includes a wireless unit 74, an antenna 84, an antenna 85, and a wireless unit 75. The command from the arithmetic and control unit 30 is received via this. Further, the solar radiation amount adjusting device 21, the spraying device 22, and the ventilation device 23 are connected to the temperature control unit 33 of the work control device 8a. The temperature control unit 33 includes the wireless unit 74, the antenna 84, the antenna 85, and the wireless unit 75. The command from the arithmetic and control unit 30 is received via

  Further, the humidity control unit 34 of the work control device 8 a is connected to the ventilation device 23, and the humidity control unit 34 issues a command from the arithmetic control device 30 via the wireless unit 74, the antenna 84, the antenna 85, and the wireless unit 75. Receive and adjust ventilation fan on / off switching and rotation speed. In addition, the soil moisture control unit 35 of the work control device 8a is connected to the pump 36, and the soil moisture control unit 35 receives commands from the arithmetic control device 30 via the wireless unit 74, the antenna 84, the antenna 85, and the wireless unit 75. , And the water absorption operation of the pump 36 is adjusted.

<Growth assistance method for vascular plants>
(Example of growth support method using correlation coefficient CL of AE generation number and light and correlation coefficient CT of AE generation number and temperature)
Next, the growth assistance method according to the first embodiment will be described. First, in step S10 of the flowchart of FIG. 19, the growth diagnosis method including steps S1 to S5 shown in FIG. 2 is performed as described above, and the correlation coefficient CL between the number of AE occurrence and light and the correlation coefficient between the number of AE occurrence and temperature. CT is calculated.

  Next, in step S11, the correlation coefficient determination unit 308 of the arithmetic and control unit 30 determines whether or not both the AE generation number and the light correlation coefficient CL and the AE generation number and the temperature correlation coefficient CT are increasing. Determine. When it is determined that at least one of the AE generation number and the light correlation coefficient CL and the AE generation number and the temperature correlation coefficient CT has a tendency to decrease or remain at a constant level, the arithmetic control device 30 It is determined that there is a possibility that the environmental factor is actively changed, and the process proceeds to step S12. On the other hand, when it is determined that both the AE generation number and the light correlation coefficient CL and the AE generation number and the temperature correlation coefficient CT are increasing, the arithmetic and control unit 30 actively changes the environmental factor. It is determined that it is not necessary, the process proceeds to step S14, and it is continuously determined whether or not to continue the growth assisting process.

  Next, in step S12, the correlation coefficient determination unit 308 of the arithmetic and control unit 30 determines whether or not both the AE generation number and the light correlation coefficient CL and the AE generation number and the temperature correlation coefficient CT are greater than or equal to a threshold value. Determine. If it is determined that at least one of the AE generation number and the light correlation coefficient CL and the AE generation number and the temperature correlation coefficient CT is less than the threshold value, the arithmetic and control unit 30 actively sets the environmental factor. It is determined that it is necessary to change, and the process proceeds to step S13. On the other hand, when it is determined that the AE generation number and the light correlation coefficient CL and the AE generation number and the temperature correlation coefficient CT are both equal to or greater than the threshold value, the arithmetic and control unit 30 actively changes the environmental factor. It is determined that it is not necessary, the process proceeds to step S14, and it is determined whether or not to continue the growth assisting process.

  Next, in step S13, the arithmetic and control unit 30 increases the correlation coefficient determined to be less than the threshold among the AE generation number and the light correlation coefficient CL and the AE generation number and temperature correlation coefficient CT. Then, a command signal is transmitted to the work control device 8a via the wireless unit 74, the antenna 84, the antenna 85, and the wireless unit 75, and an adjustment process for controlling the environmental factors as illustrated in the table of FIG. 20 is performed. Data of the environmental factor control table in FIG. 20 is stored in the data storage device 42 in advance. As shown in FIG. 20, a value of “0.6” can be set as a threshold value based on empirical rules.

  Therefore, for example, in winter, when each of the AE generation number and the light correlation coefficient CL and the AE generation number and the temperature correlation coefficient CT is less than 0.6, the arithmetic control device 30 passes the temperature control unit 33. The warming process is performed, and the supplementary light process is performed via the solar radiation amount control unit 32. In FIG. 20, “N” shown when the correlation coefficient CL between the number of AE occurrences and light and the correlation coefficient CT between the number of AE generations and temperature is 0.6 or more, It represents maintaining cultivation conditions.

  Next, in step S14, the arithmetic and control unit 30 determines whether or not to continue the growth assisting process. For this determination, for example, a display device (not shown) is connected to the arithmetic and control unit 30 and an inquiry as to whether or not to continue is displayed on this display device. Can be executed by inputting whether or not to continue.

  If it is determined that the growth assisting process is to be continued, the arithmetic and control unit 30 proceeds to step S10 and repeats the processes of steps S10 to S13 described above. On the other hand, when it is determined not to continue the growth assisting process, the arithmetic and control unit 30 ends the growth assisting process. As described above, in the growth assist treatment, control is performed to intentionally increase the correlation coefficient between the environmental factor and AE by actively changing the environmental factor, thereby achieving improvement in crop yield and quality.

  In addition, in the example demonstrated using FIG.19 and FIG.20, growth support processing is performed selectively using two correlation coefficient CL of AE generation | occurrence | production number and light, and correlation coefficient CT of AE generation | occurrence | production number and temperature. Although the case has been described by way of example, the growth assisting method according to the first embodiment is based on the correlation coefficient CH between the AE occurrence number and humidity, the correlation coefficient CS between the AE occurrence number and the soil moisture, or the AE occurrence number and the nutrition. It may be performed using other correlation coefficients such as a correlation coefficient of liquid concentration (EC), or may be performed by appropriately combining a plurality of correlation coefficients.

  For example, as described with reference to FIGS. 17 (a) and 17 (b), sugar content can be increased by cultivating such that the correlation coefficient CD of AE occurrence number and irrigation shows a positive value. On the other hand, if it is cultivated to show 0 or a negative value, the yield can be increased.

  As shown in FIG. 21, it can be seen that the smaller the amount of irrigation per day, the greater the AE occurrence number and the irrigation correlation coefficient CD. That is, in order to control the AE occurrence number and the irrigation correlation coefficient CD to increase, the irrigation conditions may be changed so that the amount of irrigation is reduced and drought stress is increased. In order to control the AE generation number and the irrigation correlation coefficient CD to decrease, the irrigation conditions may be changed so that the irrigation amount is increased and the drought stress is decreased.

  Specifically, the irrigation condition is such that, for example, the power supply of the pump 36 is switched by a relay such as a relay (not shown) connected to the pump 36, whereby the current AE occurrence number and the value of the irrigation correlation coefficient CD are It can be controlled to reach the target value. As the control algorithm, general PID control, fuzzy control, a control method using a neural network, and the like can be used.

  The growth assisting method according to the first embodiment can be performed not only by changing the irrigation conditions but also by using nutrient solution concentration (EC) or soil moisture change. That is, in addition to the method of calculating the predicted value of drought stress from the irrigation schedule as in the embodiment, the correlation coefficient for the soil moisture and the humidity around the plant body can also be used, and the correlation coefficient for the number of AEs and the irrigation coefficient. Diagnosis and control similar to those in the case of using a CD is possible.

(Example of growth support method for changing CL and CT via CD)
In the growth diagnosis apparatus shown in FIG. 1, the internal information data measurement system and the environmental factor fluctuation data measurement system exist independently of each other, and have a configuration in which a correlation coefficient is output as a diagnosis result. In the growth assisting method according to the first embodiment, in order to increase the correlation coefficient CL between the AE occurrence number and the light and the AE occurrence number and the temperature correlation coefficient CT, the temperature adjustment process and the light adjustment process are performed. Control was performed directly so that the values of environmental factors of solar radiation and temperature increased.

  However, in the growth assisting method according to the first embodiment, AE generation is not performed by increasing the correlation coefficient CD of the number of AE generation and irrigation, instead of directly increasing the values of environmental factors of solar radiation amount and temperature. The correlation coefficient CL between the number and light and the correlation coefficient CT between the number of AE occurrences and temperature can be varied so as to increase indirectly.

  FIG. 22 is calculated using all the experimental data obtained in the example. As shown in FIG. 22A, the correlation coefficient CD of the number of AE occurrences and irrigation is 0.15 or more and 0. The amount of change AECT in the correlation coefficient CT between the number of AE occurrences and the temperature in the range of .2 or less is about 0.35, which is the largest in all the ranges. In addition, when the correlation coefficient CD of the AE occurrence number and the irrigation is in the range of 0.1 to 0.15, the value of the change ΔCT in the correlation coefficient CT between the AE occurrence number and the temperature is in all ranges. Second largest.

  Further, as shown in FIG. 22B, when the correlation coefficient CD of the number of AE occurrences and irrigation is in the range of 0.15 to 0.2, the change amount ΔCL of the correlation coefficient CL between the number of AE generations and the light. The average value of is about 0.49, which is the largest of all the ranges. In addition, when the correlation coefficient CD between the number of AE occurrences and irrigation is in the range of 0.1 to 0.15, the average value of the change ΔCL in the correlation coefficient CL between the number of AE occurrences and the light is within the entire range. Second largest. That is, when the correlation coefficient CD of the AE generation number and the irrigation is 0.1 or more and 0.2 or less, the correlation coefficient CT between the AE generation number and the temperature and the correlation coefficient CL between the AE generation number and the light tends to increase.

  Therefore, at least one of the AE generation number and temperature correlation coefficient CT and the AE generation number and light correlation coefficient CL calculated in step S5 of FIG. 3 is a small value that is less than a certain threshold value or decreases. When it is determined that there is a tendency, the irrigation conditions may be varied so that the correlation coefficient CD of the number of AE occurrences and irrigation is 0.1 or more and 0.2 or less. At this time, if the correlation coefficient CD of AE occurrence and irrigation is less than 0.1, the irrigation amount is reduced, and if the correlation coefficient CD of AE occurrence and irrigation exceeds 0.2, the irrigation amount can be increased. That's fine.

  On the other hand, when it is determined that the correlation coefficient CT between the number of AE occurrences and temperature and the correlation coefficient CL between the number of AE occurrences and light are larger than a certain threshold value or tend to increase, What is necessary is just to maintain the current irrigation conditions. As the constant threshold value, the above-described value of 0.6 can be used.

  In this way, by adjusting the irrigation conditions so that the correlation coefficient CD of AE occurrence and irrigation is 0.1 or more and 0.2 or less, the correlation coefficient CD of AE occurrence and irrigation is controlled. The AE generation number and the temperature correlation coefficient CT and the AE generation number and the light correlation coefficient CL are changed, and both the AE generation number and the temperature correlation coefficient CT and the AE generation number and the light correlation coefficient CL are both. By performing growth support that increases both, it is possible to perform cultivation in which a high yield and a high sugar content are both stably achieved.

  Depending on the cultivation conditions, even if the correlation coefficient CD of the AE generation number and the irrigation is controlled to 0.1 or more and 0.2 or less, the correlation coefficient CT between the AE generation number and the temperature, the AE generation number and the phase of light. There may be a case where the relationship number CL cannot be increased. In this case, as shown in FIG. 16, the correlation coefficient CT between the number of AE occurrences and temperature and the correlation coefficient CL between the number of AE occurrences and light are directly increased by adjustment processing other than irrigation control. Just do it.

  The growth assisting apparatus according to the first embodiment can automatically optimize environmental factors using a computer system including the arithmetic control device 30, the data measuring device 7a, and the work control device 8a shown in FIG. it can. On the other hand, the growth assisting apparatus according to the first embodiment displays the optimum adjustment processing to be performed, determined by the arithmetic and control unit 30, for the producer who actually performs the work in the greenhouse 10. Depending on the optimum conditions of the adjustment process determined by the producer, specific work such as ventilation, warm air, fine fog cooling, temperature adjustment by a light-shielding curtain or sunlight adjustment may be performed. Regardless of whether it is an automatic process by a computer system or a semi-automatic process with human judgment, the grower assisting method according to the first embodiment allows the producer to perform an appropriate operation for assisting the growth. , It can be carried out at an appropriate timing.

--Second Embodiment--
<Growth diagnostic equipment for vascular plants>
As shown in FIG. 7 (b), it can be seen that in outdoor cultivation, the number of AEs generated at night is remarkably small compared to daytime. Therefore, the present inventor can sufficiently perform the growth diagnosis of the cultivated plant using only the correlation coefficient between the internal information generated in the daytime and the environmental factor affecting the internal information. And gained knowledge.

The growth diagnostic apparatus according to the second embodiment generates an amount of solar radiation of 20 W / m ² or more from the morning even on a cloudy day from the data of the amount of solar radiation, which is an environmental factor shown in FIG. 7B. Paying attention to this, as shown in the block diagram of FIG. 23, solar power generation is performed using the amount of solar radiation, and the generated electricity is converted into an internal information measurement system (1a, 1b, 11) and an environmental factor measurement system (12 A stabilized power supply (61, 62) is provided to supply to the data measuring device 7b having ˜15, 51a), and the growth diagnostic device is operated independently in the daytime.

  The internal information measurement system (1a, 1b, 11) for measuring the internal information data indicating the growth state of the vascular plant 2a shown in FIG. 23, and the fluctuation data of the environmental factors affecting the growth of the vascular plant 2a A growth diagnostic apparatus comprising a computer system comprising an environmental factor measurement system (12-15, 51a) to be measured, and an arithmetic control device 30 for calculating a correlation coefficient between internal information data and environmental factor variation data is calculated. The growth state of the vascular plant 2a is diagnosed based on the correlation coefficient value.

  The internal information measurement system (1a, 1b, 11) includes a first AE sensor 1a and a second AE sensor 1b which are sensors for measuring internal information attached to the shaft portion of the vascular plant 2a, and a first AE sensor 1a and a second AE sensor. The internal information measuring device 11 connected to 1b is provided. The environmental factor measurement system (12-15, 51a) is connected to the solar radiation meter 12, the thermometer 13, the hygrometer 14, and the soil moisture sensor 15, and the solar radiation meter 12, the thermometer 13, the hygrometer 14, and the soil moisture sensor 15. The environmental factor measurement control unit 51a is provided.

  Further, the data measuring device 7b of the growth diagnosis device is provided with a stabilized power source (61, 62) having a power module 62 and a solar cell (solar cell) 61 connected to the power module 62. The growth diagnostic apparatus according to the second embodiment is equivalent to the members of the same name in the first embodiment with respect to the structure other than the stabilized power supply (61, 62). A description is omitted, and a description will be given mainly of the photovoltaic power generation system and the self-supporting structure.

  The data measurement device 7b including the internal information measurement device 11 and the environmental factor measurement control unit 51a is, for example, a DC5V power source output from the stabilized power source (61, 62), and is sufficiently operated with a power consumption of about 0.1W. Is possible. Moreover, it is also possible to transmit data such as measured values and correlation coefficients from the data measuring device 7b to the arithmetic and control unit 30 via the wireless unit 73 and the antenna 83 with a power consumption of about 0.1 W via the wireless LAN. is there.

The solar cell 61 has, for example, a rectangular shape with a main surface of about 25 cm × 15 cm, and generates electricity to be supplied to the data measuring device 7b. In consideration of the power consumption of the data measuring device 7b, the solar cell 61 is preferably capable of obtaining an output of 0.1 W or more so as to obtain an output of about 5 W with respect to a solar radiation amount of 1000 W / m ² , for example. That is, the solar cell 61 is configured so that the data measuring device 7b provided in the outdoor greenhouse 10 can be sufficiently operated during the daytime when a solar radiation amount of 20 W / m ² is obtained during the daytime. .

  The electricity generated by the solar cell 61 is sent to the data measuring device 7b, and a part thereof is used to drive the internal information measuring system (1a, 1b, 11). The other part of the generated electricity is used to drive the environmental factor measurement system (12-15, 51a). The internal information measurement system (1a, 1b, 11) and the environmental factor measurement system (12-15, 51a) measure only in a time zone in which power generation is possible.

  The power supply module 62 is equipped with an electric double layer capacitor (supercapacitor) having excellent charge / discharge cycle characteristics, rapid charge / discharge characteristics, operating temperature range characteristics, and the like. Further, a secondary battery may be optionally incorporated in the power supply module 62 to further stabilize the power supply module 62.

  In addition to the configuration shown in FIG. 23, the data measuring device 7b may be connected to the arithmetic control device 30 by wire. In addition, the measurement data may be configured to be transmitted wirelessly to the arithmetic and control unit 30 each time measurement is performed via the internal information measurement device 11 and the environmental factor measurement control unit 51a, or each time measurement is performed. Instead, by storing data in the temporary storage device 71, a predetermined time width may be collectively transmitted to the arithmetic control device 30 at a predetermined timing during operation.

  Further, the temporary storage device 71 is configured to be a data storage device capable of writing data to a storage medium such as an SD card, and after the measurement data is stored in the storage medium, the storage medium is used by an operator, drone, or robot. You may comprise so that it may collect | recover during the patrol of such a working machine. In particular, if the work machine is used for collection, labor saving can be further promoted and the burden on the operator can be further reduced. This has a great effect in that it can be stably operated with minimal burden on the operator in summer when the heat is severe and winter when the cold is severe. As described above, the growth diagnosis apparatus according to the second embodiment shown in FIG. 23 can perform the diagnosis of the growth state of the vascular plant 2a only in the daytime when there is sunlight.

  As shown in the graph of FIG. 24, even in winter when the amount of solar radiation is small, the correlation coefficient CT between the number of AE occurrences and temperature indicated by the solid line and the correlation coefficient CL between the number of AE occurrences indicated by the broken line and the light CL. Each rough tendency appears in the same manner as in the case shown in FIG. That is, according to the growth diagnosis method according to the second embodiment, sufficiently practical processing is possible even with only daytime measurements. Needless to say, the first embodiment in which the measurement is performed for 24 hours can improve the accuracy compared with the case of the measurement only in the daytime.

  If the growth diagnosis method is performed using the growth diagnosis apparatus according to the second embodiment, in the outdoor greenhouse 10, by supplying stable power without using an external power source by solar power generation in the daytime, It becomes possible to operate independently without depending on an external power source and to perform growth diagnosis. Other effects of the growth diagnosis method according to the second embodiment are the same as those of the first embodiment.

--Third embodiment--
<Growth diagnostic equipment for vascular plants>
In the growth diagnostic apparatus according to the first and second embodiments, the AE occurrence number data is used as internal information data, and the AE occurrence number and environmental factors of the vascular plant 2a (light, temperature, humidity, rhizosphere moisture). However, the internal information is not limited to the number of AE occurrences, and other internal information of the vascular plant 2a can be used. The correlation coefficient between the information and the environmental factor is calculated, and the calculated correlation coefficient is added to the correlation coefficient between the AE occurrence number data and the fluctuation data of the environmental factor to perform growth diagnosis.

  Other internal information other than the number of AE occurrences in the vascular plant 2a includes, for example, the transpiration flow rate (transpiration flow rate) inside the stem and stem, the diameter of the stem and stem, the hardness of the stem, stem and leaf (water potential) ) And biopotential of stems, stems and leaves.

  The transpiration flow rate can be measured as internal information by a stem heat balance method, for example, using a sap flow sensor (Sap Flow Sensor, Dynamax Inc., etc.) to calculate the temperature difference between the top and bottom of the heated stem. Changes in the diameter of the stem or stem can be measured as internal information if a diameter gauge using a strain gauge or a laser displacement meter is attached to the stem or stem. Moreover, the hardness of a stem, a stem, or a leaf can be measured as internal information by a sensor using an electret element, for example. The biopotential may be measured as internal information by attaching an electrode to a stem, stem, or leaf.

  In the description of the growth diagnostic apparatus according to the third embodiment below, the number of AE occurrences is “first internal information”, and the hardness of the stem is selected as the internal information of the vascular plant 2a. The case where the hardness is used as the “second internal information” in addition to the first internal information and used in the growth diagnosis method will be described as an example.

As shown in the block diagram of FIG. 25, the growth diagnostic apparatus according to the third embodiment is a first internal information measurement system that measures data of first internal information indicating the growth state of the vascular plant 2a ( 1a, 1b, 11a), environmental factor measurement system (12-15, 51a) for measuring fluctuation data of environmental factors affecting the growth of vascular plant 2a, first internal information data and environmental factors And an arithmetic control device 30z for calculating a correlation coefficient of the fluctuation data.
The growth diagnostic apparatus according to the third embodiment further includes a second internal information measurement system (101, 11b) that measures data of second internal information indicating the growth state of the vascular plant 2a. The arithmetic and control unit 30z also calculates the correlation coefficient between the first internal information data and the second internal information data, and the correlation coefficient between the second internal information data and the environmental factor variation data.

  A first AE sensor 1a and a second AE sensor 1b are attached to the shaft portion of the vascular plant 2a as first internal information measuring sensors, and an internal information measuring device 11 is attached to the first AE sensor 1a and the second AE sensor 1b. It is connected.

  Moreover, the growth diagnostic apparatus according to the third embodiment is an environmental factor measurement provided so as to be interposed between each of the solar radiation meter 12, the thermometer 13, the hygrometer 14, and the soil moisture sensor 15 and the arithmetic control device 30z. A control unit 51a is provided. The growth diagnostic apparatus according to the third embodiment includes a solar cell 61 (solar cell) connected to the internal information measuring device 11 and the environmental factor measurement control unit 51a via a power supply module 62.

  The growth diagnostic apparatus according to the third embodiment has the same members in the first and second embodiments except for the second internal information measurement system (101, 11b) and the arithmetic control unit 30z. Since these are equivalent to each other, redundant description thereof will be omitted, and the second internal information measurement system (101, 11b) and the arithmetic control device 30z will be mainly described below.

  The hardness measurement sensor 101 constituting the second internal information measurement system (101, 11b) is attached to the stem of the vascular plant 2a, and the second internal information measurement sensor is a second sensor of the vascular plant 2a. Measure fluctuation data of hardness (water potential) as internal information. The hardness measurement sensor 101 is connected to the second internal information measurement device 11b constituting the second internal information measurement system (101, 11b), and hardness measurement data is input from the hardness measurement sensor 101. .

  As shown in the block diagram of FIG. 26, the arithmetic and control unit 30z includes an AE data sequence creation unit 301 that creates a data sequence of the number of AE occurrences in a certain time width necessary for calculating the correlation coefficient. . The arithmetic and control unit 30z also includes a solar radiation data string creation unit 302 that creates a solar radiation data string in a certain time width necessary for calculating a correlation coefficient, and a temperature data string that creates a temperature data string. It has a creation unit 303, a humidity data sequence creation unit 304 that creates a humidity data sequence, and a soil moisture data sequence creation unit 305 that creates a soil moisture data sequence.

  The arithmetic and control unit 30z includes a hardness data string creating unit 306 that creates a stem hardness data string as second internal information in a certain time width. The arithmetic and control unit 30z also includes the number of AE occurrences as the first internal information in a certain time width, the first correlation coefficient with each of the amount of solar radiation, temperature, humidity, and soil moisture, and the certain time width. It has a correlation coefficient calculation unit 307 that calculates stem hardness as the second internal information and a second correlation coefficient between each environmental factor. The arithmetic control device 30z includes a correlation coefficient determination unit 308 that determines whether each of the calculated first and second correlation coefficients is equal to or greater than a certain threshold value.

  Further, as shown in the schematic diagram of FIG. 27, the arithmetic control device 30z has a third phase relationship between the number of AE occurrences as the first internal information and the stem hardness as the second internal information in a certain time width. You may comprise so that a number may be calculated. The correlation coefficient determination unit 308 may be configured to determine whether or not the third correlation coefficient is equal to or greater than a certain threshold value.

  The hardness measurement sensor 101 measures the change in stem hardness accompanying the fluctuation of the water potential of the stem as second internal information and inputs it to the arithmetic control device 30z. As the stem hardness data, which is the second internal information used for calculating the correlation coefficient, the amount of change in stem hardness can be used, but the amount of change in stem hardness is the amount of change in water potential. Correspondingly, an increase in measured stem hardness means that the water potential has decreased. As the hardness measurement sensor, a hardness measurement sensor (dual electret sensor) 101 as illustrated in the cross-sectional view of FIG. 28 is used.

  In FIG. 28, the first diaphragm (126a, 125a), the first electret layer 123a, and the first back electrode 122a constitute a receiving element 101a, and the second diaphragm (126b, 125b) and the second electret layer 123b. The second back electrode 122b constitutes the transmitting element 101b. With the dual electret sensor 101 shown in FIG. 28, the vibration of the transmitting element 101b at the time of transmission can be known using the receiving element 101a.

  In the dual electret sensor 101 shown in FIG. 28, the first diaphragm (126a, 125a) is in contact with the first vibration electrode 126a and the lower surface (second main surface) of the first vibration electrode 126a. The second diaphragm (126b, 125b) includes a second diaphragm electrode 126b and a bottom surface (second principal surface) of the second diaphragm electrode 26b in contact with the first principal surface. Two diaphragm insulating layers 25b are provided. If the dual electret sensor shown in FIG. 28 is used, the water potential can be measured while deforming the hardness measurement sensor in accordance with the shape of the stem or leaf of the plant with a flexible structure.

  For example, the first vibrating electrode 126a shown in FIG. 28, the first diaphragm insulating layer 125a provided on the lower surface of the first vibrating electrode 126a, the first electret layer 123a facing the first diaphragm insulating layer 125a, 29 (a) using a plurality of first spacers formed on the upper surface of the first electret layer 123a and the first back electrode 122a bonded to the lower surface of the first electret layer 123a, A flexible film electret sensor is prepared. Furthermore, the first back electrode side extraction electrode GND is provided on a part of the first back electrode 122a of the film electret sensor, and the first vibration electrode side extraction electrode O2 is provided on a part of the first vibration electrode 126a side. Thus, as shown in FIG. 29 (b), the first film electret sensor forming a receiver as shown in FIG. 30 (a) is folded and bonded via the first fold line A-A '. 101a is formed.

  Similarly, as shown in FIG. 30 (b), a second film-like electret sensor 101b that forms a transmitter is formed, and as shown in FIG. 30 (c), the first film-like electret sensor 101a and the first film-like electret sensor 101a are formed. If two film-like electret sensors 101b are bonded together, a flexible dual electret sensor 101 that can be bonded to the vascular plant 2a is completed. The first film-like electret sensor 101a and the second film-like electret sensor 101b of the dual electret sensor 101 are bonded together by an interlayer adhesive layer 120 as shown in FIG.

  The water potential can be measured by pressing the dual electret sensor 101 shown in FIG. 30 while deforming it according to the shape of the stem or leaf of the vascular plant 2a such as cherry tomato. Furthermore, as shown in FIG. 31, after the dual electret sensor 101 is pressed against a plant stem or the like of the sample 2t to be measured, a protective film 135 such as a plastic film may be pressed thereon.

  According to the flexible dual electret sensor 101 as shown in FIG. 28, a measured sample made of a soft material having a durometer hardness of 100 or less, with one film-like electret sensor constituting the dual electret sensor 101 as a transmitting element 101b. If the vibration is transmitted to 2t, and the other film-like electret sensor is used as the receiving element 101a to receive the vibration from the sample 2t to be measured and the transmitted / received waveform is measured, the peak intensity and the center-of-gravity frequency of the transmitted / received waveform are used. It is possible to measure the hardness and pressing pressure of the sample to be measured 2t made of a soft material. The peak intensity is the size of the first peak of the centroid frequency of the soft material constituting the sample 2t to be measured and the fast Fourier transform (FFT) spectrum of the transmitted / received waveform.

Between the peak intensity Vp and the center-of-gravity frequency fw, there is a relationship of the following formula (2).
c = fw (Vp) ^a (2)
The center-of-gravity frequency fw is defined by the following equation (3), where i is a positive integer from 1 to n.
fw = Σ (I (i) × f (i)) / ΣI (i) (3)

  If the value of the coefficient a in the equation (2) is determined, the durometer hardness (type A) of the sample 2t to be measured has a proportional relationship with the coefficient c within a certain hardness range. It is possible to calculate durometer hardness as internal information.

(Experimental example)
Next, the sample 2t to be measured is a vascular plant 2a which is a cherry tomato, and the dual electret sensor 101 shown in FIG. 28 is pasted on the stem of the cherry tomato, and the hardness of the stem as second internal information. An experimental example in which a transmitted / received waveform is analyzed when (water potential) is measured will be described.

  In the experimental example, a single cherry tomato planted in a non-woven pot 3a and cultivated in a thermostatic chamber was selected, and the dual electret sensor 101 was attached to the cherry tomato. As illustrated in FIG. 25, the dual electret sensor 101 is fixedly installed on a stem near the root below the first AE sensor 1a and the second AE sensor 1b so as to be sandwiched between the stems by a clip.

  For cherry tomatoes, lighting was applied for 12 hours a day to form a daytime state in cultivation, and a lighting and lighting interval was provided for 12 hours to form a nighttime state. In addition, not only sunlight but also artificially formed light can be used as an environmental factor of light in the present invention. Moreover, during the cultivation period, the mist of the nutrient solution was sprayed regularly on the roots of cherry tomatoes every 450 seconds.

  Then, a step voltage of about 20 V was input to the transmitter of the dual electret sensor 101 at intervals of 10 minutes, and a signal from the receiver at that time was acquired. As a result of analyzing the obtained transmission / reception waveform, the peak intensity Vp and the center-of-gravity frequency fw of the soft material constituting the cherry tomato were calculated as shown in FIG.

  As shown by the loci of the diamond-shaped data points in FIG. 32, the peak intensity Vp tends to be larger in the daytime than in the nighttime. Further, as shown by the locus of triangular data points in FIG. 32, it is recognized that the center-of-gravity frequency fw tends to be smaller in the daytime than in the nighttime. Then, assuming that the coefficient a is 0.14, the behavior of the coefficient c calculated from the equation (2) and the AE occurrence number NAE per hour is shown in the graph of FIG. As shown by the locus of the data points of the perfect circle in FIG. 33, it can be seen that the coefficient c decreases in the daytime and increases and recovers at nighttime. Since the coefficient c has a negative correlation with the hardness, FIG. 33 shows that the hardness of the stalk increases during the day and decreases at night.

  That is, the fluctuation of the decrease in daytime water potential and the increase in nighttime water potential is reflected in the behavior of stem hardness, which is the second internal information. The behavior of the coefficient c is relatively consistent with the fluctuation of the water potential. On the other hand, as shown by the locus of square data points in FIG. 33, the AE occurrence number NAE as the first internal information increases during the day and decreases at night.

  By using the coefficient c in this way, the hardness of the vascular plant 2a as the second internal information can be measured simultaneously with the measurement of the number of AE occurrences as the first internal information. As described above, the third correlation coefficient between the first internal information and the second internal information can be obtained. Just by attaching a dual electret sensor to the stem portion of the vascular plant 2a as in this experimental example, it becomes easy to measure the water potential as indirect internal information. Then, the first to third correlation coefficients can be calculated and used for diagnosis of the growth state of the vascular plant 2a.

<Diagnosis method for growth of vascular plants>
Next, a growth diagnosis method according to the third embodiment will be described. First, in step S21 of the flowchart of FIG. 34, the arithmetic and control unit 30z selects only AE caused by cavitation as the first internal information via the first internal information measuring device 11a, and the step S1 of FIG. Similarly, data on the number of occurrences of the selected AE is measured.

  Next, in step S22, the arithmetic and control unit 30z measures environmental factor variation data in the same manner as in step S2 of FIG. That is, the arithmetic and control unit 30z includes at least one of the amount of solar radiation measured by the pyranometer 12, the temperature measured by the thermometer 13, the humidity measured by the hygrometer 14, and the soil moisture measured by the soil moisture sensor 15. Is input over time, and the arithmetic and control unit 30z accumulates at least one of the input solar radiation amount data, temperature data, humidity data, and soil moisture data in the data storage device 42. The measurement process of the environmental factors (light, temperature, humidity, rhizosphere moisture) in step S22 is performed simultaneously with the first internal information measurement process in step S21.

  Next, in step S23, the arithmetic and control unit 30z measures stem hardness data, which is second internal information, via the hardness measurement sensor. The measurement process of the first internal information in step S21, the measurement timing of the environmental factor in step S22, and the measurement timing of the second internal information in step S23 are performed so that they overlap each other in time series.

  Next, in step S24, the AE data string creation unit 301 of the arithmetic and control unit 30z generates the first internal information at the detection time of the first internal information stored in the data storage device 42 or at a certain sampling interval s. Using the number, a data group of the first internal information is created as in step S3 of FIG. The AE data string creation unit 301 creates a data string for calculating the first and third correlation coefficients using the data group of the first internal information.

  Next, in step S25, the solar radiation amount data string creation unit 302 of the arithmetic and control unit 30z, as in step S4 of FIG. 3, environmental factors (light, temperature, humidity, rhizosphere moisture) accumulated in the data storage device 42. Each data group is created using at least one of the fluctuation data. Then, the solar radiation amount data string creation unit 302 creates a data string for calculating the first and second correlation coefficients using the created data group.

  Next, in step S26, the hardness data string creation unit 306 of the arithmetic and control unit 30z uses the stem hardness data, which is the second internal information stored in the data storage device 42, to obtain the stem hardness data. Create a group. Then, the hardness data string creating unit 306 creates a data string for calculating the second and third correlation coefficients using the data group of the stem hardness.

Next, in step S27, the correlation coefficient calculation unit 307 of the arithmetic and control unit 30z uses the first internal information data string and the environmental factor data string for step S5 in FIG. Similarly, the first correlation coefficient is calculated using equation (1).
Next, in step S28, the correlation coefficient calculation unit 307 of the arithmetic and control unit 30z uses Equation (1) for a certain time width tw using the environmental factor data string and the second internal information data string. To calculate a second correlation coefficient.
Next, in step S29, the correlation coefficient calculation unit 307 of the arithmetic and control unit 30z uses the data sequence of the first internal information and the data sequence of the second internal information to calculate the expression (1 ) To calculate the third correlation coefficient.

  Calculation of the first correlation coefficient relating to the fluctuation of the first internal information and the environmental factor, calculation of the second correlation coefficient relating to the fluctuation of the second internal information and the fluctuation of the environmental factor, and the first internal information and the first The calculation of the third correlation coefficient related to the fluctuation of the internal information 2 is performed with the same time width tw. The growth diagnosis is performed using the first to third correlation coefficients.

  According to the growth diagnostic method according to the third embodiment, by using a plurality of first to third correlation coefficients, more information is used than in the case of the first and second embodiments. The growth of the vascular plant 2a can be diagnosed with higher accuracy.

  Moreover, in order to convert the measured value of stem hardness into a water potential, calibration processing is usually required for the measured value. In this respect, since the first to third correlation coefficients are used in the third embodiment, the constituent value of the measured value of the stem hardness is not essential, and the change amount of the measured value, that is, the relative change is measured. You can do it. Therefore, the calibration process of the measured value is not necessary, and the process can be facilitated even by using the water potential as indirect internal information.

Moreover, in the growth diagnostic method according to the third embodiment, the water potential inside the stem, which is indirect internal information, and the response state of the first internal information are quantified. More detailed information on the moisture status of 2a can be obtained and used for growth diagnosis. Other effects of the growth diagnosis method according to the third embodiment are the same as those in the first and second embodiments.
Similarly to the case of the first embodiment, if the growth assisting process is performed using the growth diagnosis method according to the third embodiment, using the first to third correlation coefficients, More accurate cultivation control can be performed.

-Other embodiments-
Although the present invention has been described with the first to third embodiments as described above, it should not be understood that the description and drawings constituting a part of this disclosure limit the present invention. From this disclosure, it should be understood that various alternative embodiments, examples, and operational techniques will become apparent to those skilled in the art.

  For example, as shown in the schematic diagram of FIG. 35, the solar battery 61 a and the power supply module 62 may be built in the data measuring device 7 c. Alternatively, the internal information of the vascular plant 2a and the fluctuation data of the environmental factors may be directly transmitted to the arithmetic and control unit 30 by wire without individually configuring the portable data measuring device and the work control device as shown in FIG. A growth diagnostic apparatus can also be configured.

  On the other hand, as schematically shown by the block diagram of FIG. 37, for a certain purpose, the internal information measuring device 11 and the environmental factor measurement control units 51b and 51c are configured as independent sets from each other. A configuration in which the temporary storage devices 71a to 71c and the wireless units 73a to 73c are connected is also possible. In FIG. 37, the internal information measuring device 11 to which the number of AE occurrences as internal information from the first AE sensor 1a and the second AE sensor 1b is input, and fluctuation data from the thermometer 13, the hygrometer 14, and the soil moisture sensor 15. The environmental factor measurement control unit 51b to which is input and the environmental factor measurement control unit 51c to which variation data from the pyranometer 12 is input are illustrated as being spatially separated. That is, the set is provided individually without being configured integrally with the data measuring device 7a.

  The data on the number of AE occurrences as internal information input to the internal information measuring device 11 is stored in the temporary storage device 71a, and the wireless unit 73a transmits the data stored in the temporary storage device 71a via the antenna 83a. It transmits to the arithmetic and control unit 30 by wireless LAN. The environmental factor fluctuation data input to the environmental factor measurement control unit 51b is stored in the temporary storage device 71b, and the wireless unit 73b transmits the data stored in the temporary storage device 71b to the wireless LAN via the antenna 83b. To the arithmetic and control unit 30. The environmental factor fluctuation data input to the environmental factor measurement control unit 51c is stored in the temporary storage device 71c, and the wireless unit 73c converts the data stored in the temporary storage device 71c to the wireless LAN via the antenna 83c. To the arithmetic and control unit 30.

  When the internal information measurement device 11 and the environmental factor measurement control units 51b and 51c are individually provided as in the configuration illustrated in FIG. 37, it is possible to use a combination of existing commercially available measurement devices. It becomes possible, and the manufacturing cost of the growth diagnostic apparatus can be reduced.

  In FIG. 1, FIG. 23, FIG. 25, FIG. 35, and FIG. 36 that have already been described, the case where there is only one environmental factor measurement control unit 51a is shown as an example for the sake of space. However, as exemplified by the two environmental factor measurement control units 51b and 51c in FIG. 37, the environmental factor measurement control unit is not limited to one. When a plurality of sampling plants are selected, a plurality of sampling plants are selected. Multiple units can be installed in the vicinity. For example, when four environmental factors are used, a set of four environmental factor measurement control units can be provided in the vicinity of one sampling plant.

  As already described in the first embodiment, even in the same facility such as the greenhouse 10, the value of the environmental factor may vary greatly depending on the location. Therefore, the measured value of the environmental factor at a specific point. Even if the correlation coefficient is calculated using, the growth state of the vascular plant 2a cultivated in another place away from the point may not be properly diagnosed. Therefore, also in the configuration illustrated in FIG. 37, a plurality of sets including a plurality of environmental factor measurement control units 51b and 51c are prepared, and appropriate in the vicinity of the vascular plant 2a serving as a sampling plant from which the plurality of sets are selected. By providing each of the vascular plants 2a individually, the growth diagnosis of the vascular plant 2a can be performed more appropriately.

  In particular, in the same manner as already described in the first embodiment, each of the environmental factor measuring sensors (12 to 15) is an internal information measuring sensor from the viewpoint of appropriately performing the growth diagnosis of the vascular plant 2a. It is desirable to arrange them as independent groups constituting the set at appropriate positions near the periphery of the sampling plant to which (1a, 1b) is attached. In FIG. 1 to FIG. 37, the space that can be illustrated is limited, and therefore the positions of the environmental factor measurement sensors (12 to 15) and the corresponding environmental factor measurement control units 51a, 51b, and 51c are schematically illustrated for convenience. It is located on the outer wall of the greenhouse 10, the outside of the greenhouse 10, etc., and is shown as if it is centrally managed. However, the solar radiation meter 12, the thermometer 13, the hygrometer 14, and the soil moisture sensor 15 that form the environmental factor measurement sensors (12 to 15) are particularly close to the vascular plant 2 a that is a sampling plant inside the greenhouse 10, For example, it is practically preferable that the vascular plant 2a is arranged in a plurality of sets divided into a plurality of groups at positions as close as possible within a range where the vascular plant 2a is not in contact with each other and can provide distributed information. As already mentioned.

  In the first to third embodiments, as the external environmental factors of the vascular plant 2a, the correlation coefficient for the first or second internal information is calculated for all of temperature, humidity, sunshine, and soil moisture. Diagnosis of growth may be performed. However, it is not essential to calculate all the correlation coefficients for a plurality of environmental factors, and the present invention can be configured by using at least one environmental factor. As the number of environmental factors used increases, the correlation coefficient obtained increases, and more information can be used to improve the accuracy of the growth diagnosis method and the growth assistance treatment. It is preferable to calculate the correlation coefficient using

  In the first to third embodiments, the case where the growth diagnosis apparatus that is a computer system spontaneously executes the growth diagnosis method has been described. However, instead of the growth diagnosis apparatus, processing equivalent to the growth diagnosis method is performed. The growth diagnosis method can be configured by manually performing the above. Further, as in the case of the growth diagnosis device, instead of the growth assisting device, the growth assisting method can be configured if the worker manually performs processing equivalent to the growth assisting processing.

  In addition, the growth diagnosis program and the growth assistance program are configured by configuring the programs to cause the arithmetic control devices 30 and 30z to execute the steps of the growth diagnosis method and the growth assistance method according to the first embodiment, respectively. it can. In order to execute the growth diagnosis program and the growth assistance program, for example, the growth diagnosis program and the growth assistance program are stored in a program storage device (not shown) included in the arithmetic control devices 30 and 30z, respectively. The auxiliary program may be read from the program storage device as appropriate and executed.

  The present invention can also be configured by combining a part of the structures disclosed in the first to third embodiments. As described above, the present invention includes various embodiments and the like that are not described in the present specification and drawings, and the technical scope of the present invention is specified by the invention according to the scope of claims reasonable from the above description. It is determined only by.

1a 1st AE sensor 1b 2nd AE sensor 2a, 2b Vascular plant (tomato)
3a, 3b Nonwoven fabric pot 4 Cultivation beds 7a-7c Data measuring device 8a Work control device 10 Plastic house 11 Internal information measuring device 11a First internal information measuring device 11b Second internal information measuring device 12 Pyrometer 13 Thermometer 14 Humidity Total 15 Soil moisture sensor 21 Solar radiation amount adjustment device 22 Spraying device 23 Ventilation device 24 Air conditioning device 25 Irrigation device 30, 30z Arithmetic control device 301 AE data sequence creation unit 302 Solar radiation amount data sequence creation unit 303 Temperature data sequence creation unit 304 Humidity data Sequence creation unit 305 Soil moisture data sequence creation unit 307 Correlation coefficient calculation unit 306 Hardness data sequence creation unit 308 Correlation coefficient determination unit 32 Solar radiation amount control unit 33 Temperature control unit 34 Humidity control unit 35 Soil moisture control unit 36 Pump 41 Input device 42 Data storage device 51a, 51b, 51c Environmental factor measurement control unit 61, 61a Solar cell 62 Power supply module 71, 71a, 71b, 71c Temporary storage device 72 Data bus 73, 73a, 73b, 73c, 74, 75 Wireless unit 83, 83a, 83b, 83c, 84, 85 Antenna 101 Hardness measurement Sensor

Claims (16)

Measuring internal information data indicating the growth state of vascular plants;
Measuring fluctuation data of environmental factors that influence the growth state in a time series overlapping with the timing of measuring the data of the internal information;
Calculating a correlation coefficient between the internal information data and the environmental factor variation data;
And diagnosing the growth state based on the value of the correlation coefficient.   Acoustic emission data generated by cavitation of the vascular plant, transpiration flow data inside the vascular plant, axis diameter data of the vascular plant, hardness of the vascular plant The growth diagnosis method according to claim 1, wherein the growth diagnosis method is at least one of data and biopotential of the vascular plant.   The growth diagnostic method according to claim 1 or 2, wherein the environmental factor is at least one of light, temperature, humidity, and rhizosphere moisture.   The method for diagnosing growth according to any one of claims 1 to 3, wherein the step of measuring the internal information is performed only in the daytime when there is solar sunshine. An internal information measurement system for measuring internal information data indicating the growth state of vascular plants,
An environmental factor measurement system for measuring variation data of environmental factors affecting the growth state;
An arithmetic and control unit for calculating a correlation coefficient between the internal information data and the environmental factor variation data;
A growth diagnostic apparatus comprising: diagnosing the growth state based on the value of the correlation coefficient.   Acoustic emission data generated by cavitation of the vascular plant, transpiration flow data inside the vascular plant, axis diameter data of the vascular plant, hardness of the vascular plant The growth diagnostic apparatus according to claim 5, wherein the growth diagnostic apparatus is at least one of data and a biopotential of the vascular plant.   The growth diagnostic apparatus according to claim 5 or 6, wherein the environmental factor measurement system measures at least one variation among light, temperature, humidity, and rhizosphere moisture as the environmental factor. A data measuring device that transmits data to an arithmetic and control unit of a computer system,
An internal information measuring device that acquires data of internal information indicating the growth state of the vascular plant from an internal information measuring sensor attached to the vascular plant;
From an environmental factor measurement sensor that measures the external environment of the vascular plant, an environmental factor measurement control unit that acquires variation data of environmental factors that affect the growth state;
And the output of the data measuring device is transmitted to the arithmetic control device, so that the arithmetic control device calculates a correlation coefficient between the internal information data and the environmental factor variation data, and the correlation A data measuring apparatus for diagnosing the growth state based on a numerical value.   The sensor for measuring internal information includes a sensor for measuring data of acoustic emission generated by cavitation of the vascular plant, a sensor for measuring data of transpiration flow rate inside the vascular plant, and a diameter of the axis of the vascular plant. 9. The data measuring device according to claim 8, wherein the data measuring device is at least one of a sensor for measuring data, a sensor for measuring data of hardness of the vascular plant, and a sensor for measuring data of biopotential of the vascular plant. .   10. The data measuring device according to claim 8, wherein the environmental factor measuring sensor is a sensor that measures at least one variation among light, temperature, humidity, and rhizosphere moisture.   It further comprises a solar cell that generates electricity supplied to the internal information measuring sensor and the environmental factor measuring sensor, and the growth state is diagnosed only in the daytime when there is sunlight. The data measuring device according to any one of claims 8 to 10, wherein the data measuring device is characterized in that An instruction to obtain data of internal information indicating the growth state of a vascular plant;
An instruction to acquire variation data of environmental factors that influence the growth state in a time series overlapping with the timing of measuring the data of the internal information;
Calculating a correlation coefficient between the data of the internal information and the variation data of the environmental factor, and a command for diagnosing the growth state according to the value of the correlation coefficient;
A growth diagnosis program for causing a computer control device of a computer to execute processing of control by a series of instructions including: Measuring internal information data indicating the growth state of vascular plants;
Measuring fluctuation data of environmental factors that influence the growth state in a time series overlapping with the timing of measuring the data of the internal information;
Calculating a correlation coefficient between the internal information data and the environmental factor variation data;
Adjusting the value of the environmental factor by an adjustment amount determined by the calculated value of the correlation coefficient;
A growth assistance method comprising assisting the growth of the vascular plant. An internal information measurement system for measuring internal information data indicating the growth state of vascular plants,
An environmental factor measurement system for measuring variation data of environmental factors affecting the growth state;
An adjustment device for adjusting the value of the environmental factor;
An arithmetic control device that calculates a correlation coefficient between the internal information data and the variation data of the environmental factor, and outputs a command for controlling the adjustment device with an adjustment amount determined by the value of the correlation coefficient;
A growth assisting device characterized by assisting the growth of the vascular plant. A data measuring device that transmits data to an arithmetic and control unit of a computer system,
An internal information measuring device that acquires data of internal information indicating the growth state of the vascular plant from an internal information measuring sensor attached to the vascular plant;
From an environmental factor measurement sensor that measures the external environment of the vascular plant, an environmental factor measurement control unit that acquires variation data of environmental factors that affect the growth state;
And the output of the data measuring device is transmitted to the arithmetic control device so that the arithmetic control device calculates a correlation coefficient between the internal information data and the environmental factor variation data, and A data measuring apparatus, characterized in that the value of the environmental factor is adjusted by an adjustment amount determined by the value of the relational number to assist the growth of the vascular plant. An instruction to obtain data of internal information indicating the growth state of a vascular plant;
An instruction to acquire variation data of environmental factors that influence the growth state in a time series overlapping with the timing of measuring the data of the internal information;
Calculating a correlation coefficient between the internal information data and the environmental factor variation data;
An instruction for adjusting the value of the environmental factor by an adjustment amount determined by the calculated value of the correlation coefficient, and assisting the growth of the vascular plant;
A growth assisting program that causes an arithmetic control device of a computer to execute arithmetic control processing according to a series of instructions including:

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