JP2018166416A - Information processor, information processing method, and program - Google Patents

Abstract

To provide an information processor that can accurately predict the harvest date of a first fruit vegetable even in the case where the growth environment of the first fruit vegetable is different from the growth environment of a second fruit vegetable.SOLUTION: An information processor calculates a first index value showing the growth state of a first fruit vegetable based on first spectrum showing the intensity of monochromatic light of each of a plurality of wavelengths which constitute reflected light reflected on at least a part of the surface of the first fruit vegetable, and predicts the harvest date of the first fruit vegetable based on first environmental information showing the growth environment of the first fruit vegetable, prediction information showing the relationship between an index value showing the growth state of a second fruit vegetable of the same kind as the kind of the first fruit vegetable which is a second index value corresponding to lapse time and the lapse time, and is prediction information associated with second environmental information showing the growth environment of the second fruit vegetable, and the calculated first index value.SELECTED DRAWING: Figure 2

Description

  The present invention relates to an information processing apparatus, an information processing method, and a program.

  Research and development of techniques for predicting the harvest date of crops based on the growth state of crops such as leaf vegetables and fruit vegetables are being conducted.

  In this regard, in the harvest prediction device for predicting the crop yield from the crop image data, the type spectrum information that is information related to the spectrum specific to the type of crop and the breeding that is information related to the spectrum specific to the growing stage of the crop A storage unit that stores stage spectrum information, growing period information that is information about a period from the start of crop cultivation to harvesting, and area harvest amount information that is information about a relationship between the cropped area and the crop amount; A crop type identifying unit for identifying the type of the crop by comparing the information about the spectrum obtained from the data and the type spectrum information stored in the storage unit, and obtaining the crop type information which is information about the identified type; Compare the spectrum information obtained from the image data with the growth stage spectrum information stored in the storage unit The growing stage identifying unit for identifying the growing stage of the crop, obtaining the growing stage information that is information on the identified growing stage, and calculating the acreage area based on the image data, and the cropping that is information about the acreage area Predict crop time and yield from crop area calculation unit that obtains area information, crop type information, breeding stage information, crop area information, and growing period information and area yield information stored in storage A harvest prediction device including a harvest prediction unit is known (see Patent Document 1).

JP 2003-006612 A

  Here, it is known that the period from the cultivation start date to the harvest date varies depending on the growing environment of the crop. For example, when the temperature in the growth environment of the first crop is different from the temperature in the growth environment of the second crop of the same type as the first crop, the period from the start date of the first crop to the harvest date, The period from the cultivation start date to the harvest date is different. However, since the conventional harvest prediction apparatus does not consider such a difference in the growth environment of the crop, it may be difficult to accurately predict the crop harvest time.

  Therefore, the present invention has been made in view of the above-described problems of the prior art, and even when the growth environment of the first fruit vegetable is different from the growth environment of the second fruit vegetable, the harvest date of the first fruit vegetable is accurately predicted. An information processing apparatus, an information processing method, and a program are provided.

  One aspect of the present invention is based on a first spectral spectrum showing the intensity of monochromatic light of each of a plurality of wavelengths constituting reflected light reflected on at least a part of the surface of the first fruit vegetable. A calculation unit for calculating a first index value indicating a state; first environment information indicating a growth environment of the first fruit vegetable; and an index value indicating a growth state of a second fruit vegetable of the same type as the type of the first fruit vegetable. The prediction information indicating the relationship between the second index value corresponding to the elapsed time and the elapsed time, the prediction information associated with the second environment information indicating the growth environment of the second fruit vegetable, and the calculation And a prediction unit that predicts a harvest date of the first fruit vegetable based on the first index value calculated by the unit.

  According to another aspect of the present invention, in the information processing apparatus, the information processing apparatus includes a correction unit that corrects the prediction information based on the first environment information, and the prediction unit is corrected by the correction unit. And the structure which estimates the harvest date of a said 1st fruit vegetable based on the said 1st index value calculated by the said calculation part may be used.

  According to another aspect of the present invention, in the information processing apparatus, the first environment information is a first temperature in a growth environment of the first fruit vegetable, and the second environment information is a growth environment of the second fruit vegetable. The correction unit corrects the prediction information by multiplying the elapsed time in the prediction information by a value obtained by dividing the second temperature by the first temperature. May be.

  According to another aspect of the present invention, in the information processing apparatus, the first index value is a first wavelength and a second wavelength that are two wavelengths serving as a reference among the intensities included in the first spectral spectrum. The second index value is a value based on each intensity, and the second index value indicates the intensity of monochromatic light at each of a plurality of wavelengths constituting the reflected light reflected from at least a part of the surface of the first fruit vegetable. The structure which is a value based on each intensity | strength of the said 1st wavelength and the said 2nd wavelength among the intensity | strengths contained in may be used.

  According to another aspect of the present invention, the information processing apparatus includes: an imaging unit that can capture a captured image including intensities of a plurality of wavelengths; and a spectrum generation unit that generates the first spectrum. A configuration may be used in which the generation unit generates the first spectral spectrum based on a first captured image obtained by imaging the region including the first fruit vegetables.

  In another aspect of the present invention, the second spectrum is based on a second spectral spectrum indicating the intensity of monochromatic light at each of a plurality of wavelengths constituting the reflected light reflected from at least a part of the surface of the second fruit vegetable. A calculation unit that calculates a second index value corresponding to an elapsed time, which is an index value indicating a growth state of fruit vegetables, second environment information indicating a growth environment of the second fruit vegetables, and the calculation unit calculated by the calculation unit Prediction information, which is information indicating a relationship between the second index value and the elapsed time, based on the second index value and the second index value corresponding to the elapsed time, and the second environment information Is a prediction information generation unit that generates the prediction information associated with each other.

  In another aspect of the present invention, the first spectral spectrum indicates the intensity of monochromatic light at each of a plurality of wavelengths constituting the reflected light reflected on at least a part of the surface of the first fruit vegetable. A calculation step for calculating a first index value indicating the growth state of the fruit vegetables, first environment information indicating the growth environment of the first fruit vegetables, and a growth state of the second fruit vegetables of the same type as the first fruit vegetables Prediction information that is an index value and represents a relationship between a second index value corresponding to elapsed time and the elapsed time, and the prediction information associated with second environment information indicating a growth environment of the second fruit vegetable, And a prediction step of predicting a harvest date of the first fruit vegetable based on the first index value calculated by the calculation step.

  In another aspect of the present invention, the second spectrum is based on a second spectral spectrum indicating the intensity of monochromatic light at each of a plurality of wavelengths constituting the reflected light reflected from at least a part of the surface of the second fruit vegetable. A calculation step of calculating a second index value corresponding to an elapsed time, which is an index value indicating a growth state of fruit vegetables, second environment information indicating a growth environment of the second fruit vegetables, and the calculation step calculated by the calculation step Prediction information, which is information indicating a relationship between the second index value and the elapsed time, based on the second index value and the second index value corresponding to the elapsed time, and the second environment information A prediction information generation step of generating the prediction information associated with the information processing method.

  Another aspect of the present invention is based on the second spectral spectrum indicating the intensity of monochromatic light at each of a plurality of wavelengths constituting the reflected light reflected on at least a part of the surface of the second fruit vegetable. A second calculation step of calculating a second index value corresponding to an elapsed time, which is an index value indicating a growth state of the second fruit vegetable, second environment information indicating a growth environment of the second fruit vegetable, and the second Prediction information that is information representing a relationship between the second index value and the elapsed time based on the second index value calculated by the calculation step and the second index value according to the elapsed time. A prediction information generation step for generating the prediction information associated with the second environment information, and reflected light reflected on at least a part of the surface of the first fruit vegetable of the same type as the second fruit vegetable type. Composing A first calculation step of calculating a first index value indicating a growth state of the first fruit vegetable based on a first spectral spectrum indicating the intensity of monochromatic light at each wavelength of the first wavelength, and a first condition indicating a growth environment of the first fruit vegetable Prediction step of predicting the harvest date of the first fruit vegetable based on 1 environment information, the prediction information generated by the prediction information generation step, and the first index value calculated by the first calculation step And a program for executing.

  ADVANTAGE OF THE INVENTION According to this invention, even if it is a case where the growth environment of a 1st fruit vegetable differs from the growth environment of a 2nd fruit vegetable, the information processing apparatus which can estimate the harvest date of a 1st fruit vegetable accurately, the information processing method, and A program can be provided.

It is a figure showing an example of composition of information processing system 1 concerning an embodiment. 3 is a diagram illustrating an example of a functional configuration of a control device 36. FIG. It is a figure which shows an example of the flow of the process which the control apparatus produces | generates prediction information. It is a figure which shows an example of the object 2nd picked-up image imaged in the timing T1 which is the object 2nd picked-up image displayed on the operation screen. It is a figure which shows an example of the 2nd spectral spectrum displayed on the operation screen. It is a figure which shows an example of the 2nd spectral spectrum produced | generated based on the target 2nd captured image imaged in the timing T2 which is a timing after timing T1. It is a figure which shows an example of the 2nd spectral spectrum produced | generated based on the object 2nd captured image imaged in the timing T3 which is a timing after timing T2. It is a figure which shows an example of the graph showing the time change of the normalization vegetation index based on the intensity | strength of each of the 1st wavelength and 2nd wavelength in a 2nd spectrum. It is a figure which shows an example of the flow of the process which the control apparatus 36 estimates the prediction harvest date of the 1st fruit vegetable F1. It is a figure which shows an example of the graph showing the time change of the 2nd parameter | index value in the prediction information converted by the process of step S280.

<Embodiment>
Hereinafter, embodiments of the present invention will be described with reference to the drawings.

<Outline of processing performed by information processing system>
First, an outline of processing performed by the information processing system 1 will be described.

  The information processing system 1 is a system for predicting the harvest date of fruit vegetables before harvesting. Here, the harvest date of the fruit vegetables before harvesting is the day when the fruit vegetables can be shipped. In addition, the harvest date of the fruit vegetables after harvesting is the day when the fruit vegetables are harvested. Below, in order to distinguish from the harvest date of the fruit vegetables after the harvest, the harvest date of the fruit vegetables before the harvest will be referred to as a predicted harvest date. Moreover, below, the case where the kind of fruit vegetables is a tomato is demonstrated. In the following description, for convenience of explanation, a fruit vegetable before being harvested, which is a target vegetable for which the information processing system 1 predicts a predicted harvest date, will be referred to as a first fruit vegetable F1. The type of fruit vegetables may be other types such as eggplant and pepper instead of tomato.

  When the information processing system 1 predicts the predicted harvest date of the first fruit vegetable F1, the imaging unit causes the imaging unit to image the first fruit vegetable F1. The imaging unit is an imaging unit that can capture a captured image including intensities of a plurality of wavelengths. Hereinafter, as an example, the case where each of the plurality of wavelengths is a wavelength included in the range of the ultraviolet region to the infrared region will be described. Here, the intensities of the plurality of wavelengths are intensities of a plurality of monochromatic lights having different wavelengths constituting light incident on the imaging unit from a range that can be imaged by the imaging unit. That is, each position on the captured image captured by the imaging unit has a wavelength corresponding to each other that constitutes light received by the light receiving element on the imaging element of the imaging unit corresponding to the position. The intensities of a plurality of monochromatic lights having different values are associated with each other. Each of the plurality of wavelengths may be a wavelength included in another wavelength band instead of the wavelength included in the range.

  The information processing system 1 acquires a captured image obtained by capturing the first fruit vegetables F1 by the imaging unit from the imaging unit as a first captured image. The information processing system 1 specifies the intensities of a plurality of wavelengths associated with each position in the first region based on the acquired first captured image. A 1st area | region is an area | region corresponding to 1st fruit vegetables F1 among the area | regions of a 1st captured image. That is, the intensity of the plurality of wavelengths associated with each position in the first region is the intensity of the monochromatic light of each of the plurality of wavelengths constituting the reflected light reflected on at least a part of the surface of the first fruit vegetable. is there. The information processing system 1 calculates the average value of the intensity at each position for each of a plurality of wavelengths. Here, the average value for a certain wavelength is calculated by dividing the sum of the intensities of the wavelengths associated with the respective positions by the number of the respective positions (or the area of the first region). It is an average value. The information processing system 1 generates, as a first spectral spectrum, a spectral spectrum indicating the intensity of the plurality of wavelengths based on the average value of the intensity at each position calculated for each of the plurality of wavelengths. The information processing system 1 calculates an index value indicating the growth state of the first fruit vegetable F1 as the first index value based on the generated first spectral spectrum.

  Here, when the period from the fruit cultivation start date to the harvest date does not change according to the fruit growing environment, the information processing system 1 uses the first fruit vegetable F1 based on the growing state indicated by the calculated first index value. The expected harvest date can be predicted. For example, when rice is harvested only once a year, such as rice cultivation, in the growth of a plurality of fruit vegetables S1 that are performed only once a year in a certain region, the growth environment such as temperature differences in the region. Therefore, the information processing system 1 can predict the expected harvest date of the fruit vegetable S1 based on the growing state indicated by the calculated first index value. The fruit vegetable S1 is a kind of fruit vegetable. However, fruit vegetables may be repeatedly harvested at each of a plurality of different periods in one year. In such a case, the period from the cultivation start date of the fruit vegetable S1 to the harvest date changes depending on the growth environment for each cultivation period of the fruit vegetable S1. For example, when fruit vegetables are repeatedly cultivated at a plurality of different periods in a certain area, the average temperature of the area changes at each period. For this reason, the average temperature in the growth environment of the fruit vegetable S1 is different from the average temperature in the growth environment of the fruit vegetable S2 that is the same type of fruit vegetable S1 and grown at a different time from the time when the fruit vegetable S1 was grown. The period from the cultivation start date of S1 to the harvest date is different from the period from the cultivation start date of fruit vegetable S2 to the harvest date (does not match). Here, the information processing system X (for example, a conventional information processing system) different from the information processing system 1 assumes a situation in which harvesting is performed only once a year, such as rice cultivation, as a usage situation. Such differences in the growth environment of fruit vegetables are not taken into consideration, and it may be difficult to accurately predict the predicted harvest date of the fruit vegetables before harvest.

  Therefore, the information processing system 1 includes the first environment information indicating the growth environment of the first fruit vegetable F1 and the index value indicating the growth state of the second fruit vegetable F2 of the same type as the first fruit vegetable F1 at the elapsed time. Prediction information indicating the relationship between the corresponding second index value and the elapsed time, which is associated with the second environment information indicating the growth environment of the second fruit vegetable F2, and the calculated first index value Based on this, the predicted harvest date of the first fruit vegetable F1 is predicted. In this example, the second fruit vegetable F2 is a fruit vegetable harvested before starting to grow the first fruit vegetable F1 (that is, a fruit vegetable after harvesting). Moreover, since the kind of 1st fruit vegetable F1 in this example is a tomato, the kind of 2nd fruit vegetable F2 is a tomato. Thereby, the information processing system 1 can accurately predict the predicted harvest date of the first fruit vegetable F1 even if the growth environment of the first fruit vegetable F1 is different from the growth environment of the second fruit vegetable F2. Here, the elapsed time is an elapsed time from the start date of the second fruit vegetable F2 to the harvest date. In addition, the said cultivation start date may be the day when the seedling of the 2nd fruit vegetables F2 was planted, and may be the day when the user determined that the growth state of the 2nd fruit vegetables F2 became a predetermined state. The day on which observation of the second fruit vegetables F2 was started may be performed, or may be another day before the harvest date of the second fruit vegetables F2 based on the second fruit vegetables F2.

  Note that the information processing system 1 causes the imaging unit to image the second fruit vegetable F2 when generating the prediction information. The imaging unit is the imaging unit described above, and can capture a captured image including intensities of a plurality of wavelengths. The information processing system 1 acquires a captured image obtained by capturing the second fruit vegetable F2 by the imaging unit from the imaging unit as a second captured image. The information processing system 1 specifies the intensities of a plurality of wavelengths associated with the respective positions in the second region based on the acquired second captured image. The second area is an area corresponding to the second fruit vegetable F2 in the area of the second captured image. That is, the intensity of the plurality of wavelengths associated with each position in the second region is the intensity of the monochromatic light of each of the plurality of wavelengths constituting the reflected light reflected on at least a part of the surface of the second fruit vegetable. is there. The information processing system 1 calculates the average value of the intensity at each position for each of a plurality of wavelengths. The average value for a certain wavelength is an average value calculated by dividing the sum of the intensities of the wavelengths associated with the respective positions by the number of the respective positions. The information processing system 1 generates, as a second spectral spectrum, a spectral spectrum indicating the intensity of the plurality of wavelengths based on the average value of the intensity at each position calculated for each of the plurality of wavelengths. The information processing system 1 calculates an index value indicating the growth state of the second fruit vegetable F2 as the second index value based on the generated second spectral spectrum. The information processing system 1 predetermines such processing from the second captured image to the calculation of the second index value from the start date of the second fruit vegetable F2 to the harvest date of the second fruit vegetable F2. Repeated every time the specified period elapses. The period is 24 hours. Moreover, the information processing system 1 acquires the second environment information indicating the growth environment of the second fruit vegetables every time the period elapses. The information processing system 1 determines the second index value and the above-described elapsed time (that is, based on the acquired second environment information and a plurality of second index values calculated each time the period has passed) Prediction information that is information indicating the relationship with the elapsed time) is generated and is associated with the second environment information. Note that the period may be a period shorter than 24 hours or a period longer than 24 hours.

  In the present embodiment, the information processing system 1 that generates the prediction information and the information processing system 1 that predicts the predicted harvest date of the first fruit vegetable F1 will be described in detail. .

<Configuration of information processing system>
Hereinafter, the configuration of the information processing system 1 will be described. FIG. 1 is a diagram illustrating an example of a configuration of an information processing system 1 according to the embodiment.

  The information processing system 1 includes an imaging unit 11, a detection unit 12, an information processing device 30, an input device 33, and a display device 35. Further, the information processing apparatus 30 includes a storage device 32 and a control device 36. In the information processing system 1 in this example, the information processing device 30 is configured by the separate storage device 32 and the control device 36, respectively, but instead of this, the integrated storage device 32 and the control device 36. The information processing apparatus 30 may be configured as described above. In the information processing system 1, the information processing device 30, the input device 33, and the display device 35 are configured separately, but instead, the information processing device 30, the input device 33, A part or all of the display device 35 may be integrally formed.

  In the information processing system 1, the imaging unit 11 and the detection unit 12 are connected to the control device 36 via the network NW so as to be able to communicate with each other. The network NW is, for example, the Internet, a mobile communication network, a dedicated line communication network, a LAN (Local Area Network), a WAN (Wide Area Network), or the like. In the information processing system 1, either one or both of the imaging unit 11 and the detection unit 12 are connected to the information processing system 1 so as to be able to communicate with each other wirelessly or by wire without going through the network NW. There may be.

  The imaging unit 11 is an example of the imaging unit described above that can capture a captured image including intensities of a plurality of wavelengths, and is, for example, a spectrum camera. That is, the captured image captured by the imaging unit 11 is an optical spectrum image. The imaging unit 11 outputs the captured image to the control device 36. Here, the captured image is associated with the first date and time information indicating the shooting date and time. The captured image may be configured to be associated with information related to other imaging in addition to the first date and time information. Further, the imaging unit 11 may have a configuration having the same function as a spectrum generation unit 365 described later. In this case, the imaging unit 11 generates a spectral spectrum based on the captured image and outputs information indicating the generated spectral spectrum to the control device 36.

  The imaging part 11 is installed in the position which can image the area | region containing the fruit vegetables before harvest. Note that the imaging unit 11 can image the bottom surface of the fruit vegetable or the side surface of the first fruit vegetable F1 when the surface of the fruit vegetable has a top surface and the surface opposite to the top surface is the bottom surface. It is desirable to install in a proper position. For example, when the imaging unit 11 is installed at a position where the region including the first fruit vegetables F1 can be captured, the imaging unit 11 captures the region in response to a request from the control device 36. And the imaging part 11 outputs the captured image by which the said area | region was imaged, ie, the captured image by which the 1st fruit vegetables F1 were imaged, to the control apparatus 36 as a 1st captured image. Further, when the imaging unit 11 is installed at a position where the region including the second fruit vegetable F2 before harvesting can be imaged, the imaging unit 11 captures the region in response to a request from the control device 36. Then, the imaging unit 11 outputs a captured image in which the region is captured, that is, a captured image in which the second fruit F2 is captured, to the control device 36 as a second captured image. Note that the imaging unit 11 may be configured by a plurality of spectrum cameras instead of a single spectrum camera. In this case, the imaging unit 11 outputs, to the control device 36, a plurality of captured images obtained by capturing the entire surface of the fruit vegetables (first fruit vegetables F1 or second fruit vegetables F2) by the plurality of spectrum cameras.

  The detection unit 12 detects environmental information indicating the environment at the position where the detection unit 12 is installed. More specifically, the detection unit 12 detects a physical quantity indicating the environment as the environment information. The detection unit 12 may be configured to detect another quantity indicating the environment as the environment information instead of the configuration detecting the physical quantity indicating the environment as the environment information. Below, the case where the detection part 12 detects the temperature of the said environment as said environment information is demonstrated as an example. In other words, the detection unit 12 is an air temperature sensor (temperature sensor) in this example. The detection unit 12 outputs the detected temperature to the control device 36 as environment information. At this time, the detection unit 12 associates the environment information with second date and time information indicating the date and time when the temperature is detected. The detection unit 12 may be any sensor that detects another physical quantity indicating the environment instead of the air temperature sensor that detects the temperature of the environment. The other physical quantities are, for example, humidity, sunlight irradiation amount, and the like.

  The detection part 12 is installed in the position which can detect the temperature in the growth environment of the fruit vegetable before a harvest. For example, when the detection unit 12 is installed at a position where the first temperature in the growth environment of the first fruit vegetable F1 can be detected, the detection unit 12 uses the first temperature in the growth environment of the first fruit vegetable F1 as described above. Detect as environmental information. Then, the detection unit 12 outputs the detected first environment information to the control device 36. Moreover, when the detection part 12 is installed in the position which can detect the 2nd temperature in the growth environment of the 2nd fruit vegetables F2, the detection part 12 uses the 2nd above-mentioned 2nd temperature in the growth environment of the 2nd fruit vegetables F2. Detect as environmental information. Then, the detection unit 12 outputs the detected second environment information to the control device 36.

  When the storage device 32 is separate from the control device 36 as in this example, the storage device 32 is an HDD (Hard Disk Drive), an SSD (Solid State Drive), or the like. The storage device 32, when integrated with the control device 36, is an HDD, SSD, EEPROM (Electrically Erasable Programmable Read-Only Memory), ROM (Read-Only Memory), RAM (Random Access Memory), or the like. The storage device 32 stores various information and images processed by the control device 36, various programs, various databases, and the like.

  The storage device 32 is communicably connected to the control device 36 by a cable. Wired communication via a cable is performed according to standards such as Ethernet (registered trademark) and USB (Universal Serial Bus), for example. The storage device 32 may be configured to be connected to the control device 36 by wireless communication performed according to a communication standard such as Wi-Fi (registered trademark).

  The control device 36 is, for example, a desktop PC (Personal Computer), a notebook PC, a tablet PC, a workstation, a multifunctional mobile phone terminal (smartphone), a PDA (Personal Digital Assistant), or the like. The control device 36 may be another information processing device instead of these. The control device 36 controls the entire information processing system 1.

  When the harvesting date of the first fruit vegetable F1 is predicted, the control device 36 causes the imaging unit 11 to capture a range that can be captured by the imaging unit 11 based on an operation received from the user. Here, in this case, the imaging unit 11 is installed at a position where the region including the first fruit vegetables F1 can be imaged. The control device 36 acquires a first captured image obtained by capturing the first fruit vegetables F <b> 1 by the imaging unit 11 from the imaging unit 11. Based on the acquired first captured image, the control device 36 specifies the intensities of a plurality of wavelengths associated with each position in the first region. The control device 36 calculates the average value of the intensity at each position for each of a plurality of wavelengths. The control device 36 generates a spectral spectrum indicating the intensities of the plurality of wavelengths as the first spectral spectrum based on the average value of the intensities at the respective positions calculated for each of the plurality of wavelengths. Moreover, the control apparatus 36 makes the detection part 12 detect the air temperature in the position where the detection part 12 was installed as environmental information. Here, in this case, the detection unit 12 is installed at a position where the first temperature in the growth environment of the first fruit vegetable F1 can be detected. The control device 36 acquires the first environment information detected by the detection unit 12 from the detection unit 12.

  The control device 36 calculates a first index value indicating the growth state of the first fruit vegetable F1 based on the generated first spectral spectrum. Moreover, the control apparatus 36 is the 1st air temperature which the 1st environmental information acquired from the detection part 12 shows, the 2nd index value according to elapsed time which is an index value which shows the growth state of the 2nd fruit vegetables F2, and the said progress. Prediction information representing the relationship with time, and the predicted harvest date of the first fruit vegetable F1 based on the prediction information associated with the second temperature in the growth environment of the second fruit vegetable F2 and the calculated first index value Predict. Accordingly, the control device 36 can accurately predict the predicted harvest date of the first fruit vegetable F1 even when the growth environment of the first fruit vegetable F1 is different from the growth environment of the second fruit vegetable F2. Hereinafter, as an example, a case will be described in which the prediction information is a function representing the prediction information, that is, a function representing a change in the second index value according to the change in the elapsed time. Instead of this, the prediction information is a relationship between the second index value according to the elapsed time and the elapsed time, such as a table in which the second index value according to the elapsed time is associated with the elapsed time. It may be other information representing.

  The input device 33 is, for example, a keyboard, a mouse, a touch pad, or the like. The input device 33 may be another input device instead of these. Further, the input device 33 may be a touch panel configured integrally with the display device 35.

  The input device 33 is communicably connected to the control device 36 by a cable. Wired communication via a cable is performed according to standards such as Ethernet (registered trademark) and USB (Universal Serial Bus), for example. The input device 33 may be configured to be connected to the control device 36 by wireless communication performed according to a communication standard such as Wi-Fi (registered trademark).

  The display device 35 is a display including, for example, a liquid crystal display panel or an organic EL (ElectroLuminescence) display panel as a display unit. The display device 35 displays various images acquired from the control device 36 on the display unit.

  The display device 35 is communicably connected to the control device 36 by a cable. Wired communication via a cable is performed according to standards such as Ethernet (registered trademark) and USB (Universal Serial Bus), for example. The display device 35 may be configured to be connected to the control device 36 by wireless communication performed according to a communication standard such as Wi-Fi (registered trademark).

<Functional configuration of control device>
Hereinafter, the functional configuration of the control device 36 will be described with reference to FIG. FIG. 2 is a diagram illustrating an example of a functional configuration of the control device 36.

  The control device 36 includes an imaging unit control unit 361, a detection unit control unit 362, a display control unit 363, a captured image acquisition unit 364, a spectrum generation unit 365, an environment information acquisition unit 366, and a prediction information generation unit 367. A prediction information correction unit 368, a calculation unit 369, and a harvest date prediction unit 370. For example, a CPU (not shown) executes various programs (not shown) stored in the storage unit 32 or a storage unit 32 (not shown) such as an HDD or an SSD (not shown) built in the control device 36. It is realized by. In addition, some or all of the functional units may be hardware functional units such as LSI (Large Scale Integration) and ASIC (Application Specific Integrated Circuit).

  The imaging unit control unit 361 controls the imaging unit 11 to cause the imaging unit 11 to image a range that can be captured by the imaging unit 11.

  The detection part control part 362 controls the detection part 12, and makes the detection part 12 detect air temperature as environmental information.

  The display control unit 363 generates various images based on the operation received from the user. The display control unit 363 displays the generated image on the display unit of the display device 35.

  The captured image acquisition unit 364 acquires the captured image captured by the imaging unit 11 from the imaging unit 11. Then, the captured image acquisition unit 364 stores the acquired captured image in the spectrum DB 321 generated in the storage area of the storage device 32. When the spectrum DB 321 does not exist in the storage area, the captured image acquisition unit 364 generates the spectrum DB 321 in the storage area. In the example illustrated in FIG. 2, among the captured images stored in the spectrum DB 321 by the captured image acquisition unit 364, the first captured image is illustrated as the first prediction data D11. In this example, among the images stored in the spectrum DB 321 by the captured image acquisition unit 364, the second captured image is shown as first prediction information generation data D12.

  The spectrum DB 321 is based on at least a part of the first region corresponding to the first fruit vegetable F1 among the regions on the first captured image stored as the first prediction data D11 in the spectrum DB 321 based on the operation received from the user. A first spectral spectrum indicating the intensity of a plurality of wavelengths is generated. Moreover, spectrum DB321 is based on the operation received from the user, and the 2nd area | region corresponding to 2nd fruit vegetables F2 among the area | regions on the 2nd captured image stored as data D12 for 1st prediction information generation in spectrum DB321. A second spectral spectrum indicating the intensity of a plurality of wavelengths in at least a part is generated.

  The environment information acquisition unit 366 acquires the environment information detected by the detection unit 12 from the detection unit 12. Then, the environment information acquisition unit 366 stores the acquired environment information in the environment information DB 322 generated in the storage area of the storage device 32. In the example illustrated in FIG. 2, among the environment information stored in the environment information DB 322 by the environment information acquisition unit 366, the first environment information indicating the first temperature is indicated as the second prediction data D21. In this example, the second environment information indicating the second temperature among the information stored in the environment information DB 322 by the environment information acquisition unit 366 is indicated as second prediction information generation data D22.

  The prediction information generation unit 367 is a second index value calculated by the calculation unit 369, which will be described later, and a second index value corresponding to the elapsed time, and the second index value stored as second prediction information generation data D22 in the environment information DB 322. 2 Prediction information is produced | generated based on 2nd temperature which environmental information shows.

  The prediction information correction unit 368 corrects the prediction information generated by the prediction information generation unit 367 based on the first air temperature indicated by the first environment information stored as the second prediction data D21 in the environment information DB 322.

  The calculation unit 369 calculates a first index value indicating the growth state of the first fruit vegetable F1 based on the first spectral spectrum generated by the spectrum generation unit 365. Further, the calculation unit 369 calculates a second index value corresponding to the elapsed time, which is an index value indicating the growth state of the second fruit vegetable F2, based on the second spectral spectrum generated by the spectrum generation unit 365.

  The harvest date prediction unit 370 predicts the predicted harvest date of the first fruit vegetable F1 based on the first index value calculated by the calculation unit 369 and the prediction information corrected by the prediction information correction unit 368.

<Process in which control device generates prediction information>
Hereinafter, with reference to FIG. 3, the process in which the control apparatus 36 produces | generates prediction information is demonstrated. FIG. 3 is a diagram illustrating an example of a flow of processing in which the control device 36 generates prediction information. In the following, the imaging unit 11 has been installed at a position where the region including the second fruit vegetable F2 can be imaged, and the detection unit 12 has been installed at a position where the temperature in the growing environment of the second fruit vegetable F2 can be detected. The case will be described. In the following, a case will be described in which the control device 36 has received an operation for starting the prediction information generation processing from the user.

  The imaging unit control unit 361 and the detection unit control unit 362 wait until the above-described predetermined period elapses (step S110). Here, the imaging unit control unit 361 and the detection unit control unit 362 wait in the first process of step S110 until, for example, the time period elapses from the time when the operation of starting the prediction information generation process is received from the user. To do. In addition, the imaging unit control unit 361 and the detection unit control unit 362 wait in the second and subsequent processing in step S110 from the time when it is determined that the period has elapsed in the previous processing in step S110 until the period has elapsed. . When it is determined that the period has elapsed (step S120—YES), the imaging unit control unit 361 causes the imaging unit 11 to capture a range that can be captured by the imaging unit 11, and the detection unit control unit 362 includes the detection unit 12. The detection unit 12 is made to detect the second temperature at the installed position as the second environment information (step S120). Then, the imaging unit control unit 361 outputs the captured image captured by the imaging unit 11 to the control device 36 as a second captured image. Further, the detection unit control unit 362 outputs the second environment information detected by the detection unit 12 to the control device 36.

  Next, the captured image acquisition unit 364 and the environment information acquisition unit 366 perform a second acquisition process (step S130). Specifically, the captured image acquisition unit 364 acquires, from the imaging unit 11, the second captured image output from the imaging unit 11 to the control device 36 in step S110 in the second acquisition process. Moreover, the environment information acquisition part 366 acquires the 2nd environment information which the detection part 12 output to the control apparatus 36 in step S110 from the detection part 12 in a 2nd acquisition process.

  Next, the captured image acquisition unit 364 and the environment information acquisition unit 366 perform a second storage process (step S140). Specifically, in the second storage process, the captured image acquisition unit 364 stores the second captured image acquired from the imaging unit 11 in step S130 in the spectrum DB 321 as first prediction information generation data D12. Further, in the second storage process, the environment information acquisition unit 366 stores the second environment information acquired from the detection unit 12 in step S130 in the environment information DB 322 as the second predicted information generation data D22. Here, as described above, the first date and time information is associated with the second captured image. For this reason, in step S140, the imaging unit control unit 361 stores the second captured image in the spectrum DB 321 as first prediction information generation data D12 in time series order. Further, as described above, the second date and time information is associated with the information indicating the second temperature. Therefore, in step S140, the environment information acquisition unit 366 stores information indicating the second temperature in time series in the environment information DB 322 as the second predicted information generation data D22.

  Next, the imaging unit control unit 361 and the detection unit control unit 362 determine whether today is the harvest date of the second fruit vegetable F2 (step S150). Here, the imaging unit control unit 361 and the detection unit control unit 362 determine that today is the harvest date of the second fruit vegetable F2 by receiving information indicating that today is the harvest date from the user. That is, the imaging unit control unit 361 and the detection unit control unit 362 determine that the current date is not the harvest date of the second fruit vegetable F2 until the information indicating that the current date is the harvest date is received from the user. When it is determined that today is not the harvest date of the second fruit vegetable F2 (step S150-NO), the imaging unit control unit 361 and the detection unit control unit 362 transition to step S110, and the above-described predetermined period has elapsed. Wait again until you do. On the other hand, when the imaging unit control unit 361 and the detection unit control unit 362 determine that today is the harvest date of the second vegetable vegetable F2 (step S150-YES), the spectrum generation unit 365 and the calculation unit 369 store the first in the spectrum DB 321. The processing of step S170 to step S190 is repeated for each of the plurality of second captured images stored in time-series order as the one prediction information generation data D12 (step S160). Hereinafter, for convenience of explanation, the second captured image selected in step S160 will be referred to as a target second captured image.

  After the target second captured image is selected in step S160, the spectrum generation unit 365 selects the second region corresponding to the second fruit vegetable F2 among the regions on the target second captured image based on the operation received from the user. A designated area which is an area designated by the user is specified (step S170). The designated area is an area designated by the user and includes a position in which the intensities of a plurality of wavelengths used for generating the second spectral spectrum are associated with each other in the second area. . Here, the process of step S170 will be described.

  For example, after the target second captured image is selected in step S160, the display control unit 363 generates an operation screen that accepts an operation from the user, and displays the generated operation screen on the display unit of the display device 35. At this time, the display control unit 363 displays the target second captured image on the operation screen. FIG. 4 is a diagram illustrating an example of the target second captured image captured at timing T1, which is the target second captured image displayed on the operation screen. Hereinafter, as an example, a case will be described in which the timing T1 is the timing at which the first step S120 of the steps S110 to S150 is performed. An image G1 illustrated in FIG. 4 is an example of the target second captured image. A hatched area C1 in the image G1 indicates a second area corresponding to the second fruit vegetable F2 in the area of the image G1. Here, the user can designate a desired area in the area C1 as the designated area. The display control unit 363 displays information indicating the designated area designated by the user on the area C1 based on the operation received from the user. A frame WD illustrated in FIG. 4 is an example of information indicating a designated area. That is, in the example illustrated in FIG. 4, the intensities of the plurality of wavelengths used for generating the second spectral spectrum are the plurality of wavelengths associated with the positions included inside the frame WD among the respective positions in the region C1. Of strength. The shape of the frame WD is a rectangular shape, but may be other shapes such as a circular shape and an elliptical shape. In addition, the display control unit 363 detects a second region corresponding to the second fruit vegetable F2 in the region of the image G1 by pattern matching or the like, and detects a region that is not overexposure or blackout in the detected second region. The configuration may be such that the designated area is determined without accepting an operation from the user. At this time, the size, shape, and the like of the designated area may be determined in advance, or may be changed according to the area. After the display control unit 363 displays the frame WD on the region C1, the spectrum generation unit 365 identifies a region included in the frame WD in the region C1 as the designated region. The designated area may be the entire area C1. The designated area may be configured to include at least a part of the area other than the area C1 in the area of the image G1 together with the area C1.

  After the process of step S170 is performed, the spectrum generation unit 365 identifies the intensities of a plurality of wavelengths associated with the respective positions in the designated region identified by the display control unit 363 among the respective positions in the region C1. . The spectrum generation unit 365 calculates the average value of the intensity at each position for each of a plurality of wavelengths. The spectrum generation unit 365 generates, as a second spectral spectrum, a spectral spectrum indicating the intensity of the plurality of wavelengths based on the average value of the intensity at each position calculated for each of the plurality of wavelengths (step S180). Then, the display control unit 363 displays the second spectral spectrum generated by the spectrum generation unit 365 on the aforementioned operation screen. FIG. 5 is a diagram illustrating an example of the second spectral spectrum displayed on the operation screen.

  As shown in FIG. 5, the second spectral spectrum generated based on the image G1 captured at the timing T1 has a peak of visible light at a wavelength of about 550 nm. Visible light having a wavelength of about 550 nm is light that is recognized by humans as yellowish green. That is, it can be seen from the second spectral spectrum that the second fruit vegetable F2 included in the image G1 is not yet ripe and that the timing T1 is not the harvest time of the second fruit vegetable F2. Such a second spectral spectrum varies depending on the growth state of the second fruit vegetable F2. Moreover, the said growth state changes according to the above-mentioned elapsed time. That is, the second spectral spectrum changes according to the elapsed time.

  For example, FIG. 6 is a diagram illustrating an example of the second spectral spectrum generated based on the target second captured image captured at timing T2, which is a timing later than timing T1. FIG. 7 is a diagram illustrating an example of a second spectral spectrum generated based on the target second captured image captured at timing T3, which is a timing later than timing T2. By comparing FIG. 5 to FIG. 7, in the visible light region in the second spectral spectrum, the peak shifts from the wavelength corresponding to yellow-green to the wavelength corresponding to red over time. I understand. This represents a process in which the second fruit vegetable F2 ripens according to the elapsed time.

  Here, the intensity C11 illustrated in FIG. 5 indicates the intensity of near infrared light having a wavelength of 680 nm in the second spectral spectrum generated based on the target second captured image captured at the timing T1. In addition, the intensity C12 illustrated in FIG. 5 indicates the intensity of near-infrared light having a wavelength of 780 nm in the second spectral spectrum generated based on the target second captured image captured at the timing T1. Also, the intensity C21 illustrated in FIG. 6 indicates the intensity of near infrared light having a wavelength of 680 nm in the second spectral spectrum generated based on the target second captured image captured at the timing T2. In addition, the intensity C22 illustrated in FIG. 6 indicates the intensity of near-infrared light having a wavelength of 780 nm in the second spectral spectrum generated based on the target second captured image captured at the timing T2. In addition, the intensity C31 illustrated in FIG. 7 indicates the intensity of near-infrared light having a wavelength of 680 nm in the second spectral spectrum generated based on the target second captured image captured at the timing T3. In addition, the intensity C32 illustrated in FIG. 7 indicates the intensity of near-infrared light having a wavelength of 780 nm in the second spectral spectrum generated based on the target second captured image captured at the timing T3.

  When the near-infrared region in each of the second spectral spectra in FIGS. 5 to 7 is seen, it can be seen that the intensity of the near-infrared light having a wavelength of about 680 nm varies greatly according to the elapsed time. In this case, it can be seen that the intensity of near infrared light having a wavelength of about 780 nm hardly changes depending on the elapsed time. This is because the user selects the first wavelength of about 680 nm and the second wavelength of about 780 nm as the two wavelengths serving as the reference among the wavelengths in the near infrared region, and selects the first wavelength and the second wavelength selected. This means that the based value can be used as an index value indicating the growth state of the second fruit vegetable F2. Based on the first wavelength and the second wavelength in the near-infrared region, the control device 36 calculates an index value indicating the growth state of the second fruit vegetable F2 as the second index value. Hereinafter, as an example, a case will be described in which the controller 36 uses 680 nm as the first wavelength and 780 nm as the second wavelength. Note that each of the first wavelength and the second wavelength may be another wavelength in the entire region of the second spectral spectrum as long as the value indicating the growth state of the second fruit vegetable F2 can be calculated.

  After the process of step S180 is performed, the calculation unit 369 calculates the second index value based on the second spectral spectrum calculated by the spectrum generation unit 365 in step S170 (step S190). Specifically, the calculation unit 369 calculates a normalized vegetation index based on the intensity of the first wavelength in the second spectral spectrum and the intensity of the second wavelength in the second spectral spectrum as the second index value. Here, when the intensity of the first wavelength is expressed as V1 and the intensity of the second wavelength is expressed as V2, the normalized vegetation index based on V1 and V2 is calculated by (V2−V1) / (V2 + V1). The second index value may be another value based on V1 and V2 instead of the normalized vegetation index. This normalized vegetation index becomes smaller as the growth state of the second fruit vegetable F2 progresses because V1 increases with the passage of time and V1 does not substantially change. FIG. 8 is a diagram illustrating an example of a graph representing temporal changes in the normalized vegetation index based on the intensities of the first wavelength and the second wavelength in the second spectral spectrum. The vertical axis of the graph shows the normalized vegetation index that is the second index value. Further, the horizontal axis of the graph indicates the elapsed time described above. A curve FC1 shown in FIG. 8 represents the temporal change. FIG. 8 shows that the normalized vegetation index used as the second index value monotonously decreases with the passage of time and has a one-to-one correspondence with the elapsed time. For this reason, the 2nd index value is desirable as a value which shows the growth state of the 2nd fruit vegetables F2.

  After the process of step S190 is performed, the spectrum generation unit 365 and the calculation unit 369 transition to step S160, and select the next target second captured image from the unselected second captured images. Note that if there is no unselected second captured image in step S160, the prediction information generation unit 367 proceeds to step S200.

  Through the repetition processing of step S160 to step S190 as described above, the control device 36 has the second index value for each of the second captured images stored in the storage device 32 as the first prediction information generation data D12 in chronological order. That is, it is possible to calculate the second index value for each date and time indicated by the first date and time information associated with each of the second captured images. After the repetitive processing of step S160 to step S190 is performed, the prediction information generation unit 367 is based on the second index value calculated by the repetitive processing and for each date and time based on the second index value for each date and time. A function representing a temporal change in the second index value, that is, a function representing a change in the second index value according to a change in elapsed time is generated as prediction information (step S200). Specifically, the prediction information generation unit 367 generates a function for fitting to the curve FC1 shown in FIG. 8 as prediction information. At this time, the prediction information generation unit 367 may or may not convert the elapsed time in the prediction information to the number of remaining days (or remaining time) until the harvest date. Hereinafter, as an example, a case where the prediction information generation unit 367 does not convert the elapsed time in the prediction information into the number of remaining days until the harvest date will be described. In addition, the prediction information generation unit 367 reads a plurality of second environment information stored as the second prediction information generation data D22 in the environment information DB 322 from the storage device 32. And the prediction information generation part 367 calculates the average value of the 2nd temperature which each of the read said some 2nd environmental information shows. The prediction information generation unit 367 associates the calculated average value with the generated prediction information. Note that the prediction information generation unit 367 includes the plurality of second values such as an average value based on the second temperature indicated by each of the second environment information of the plurality of second environment information read from the storage device 32. The structure which uses the other value based on each of the 2nd environmental information of at least one part of environmental information as 2nd temperature matched with prediction information may be sufficient. After generating the prediction information in this way, the prediction information generation unit 367 stores the generated prediction information in the storage device 32 and ends the process.

  As described above, the control device 36 generates prediction information. Thereby, the control apparatus 36 can estimate the prediction harvest date of the 1st fruit vegetable F1 based on the produced | generated prediction information.

<Process in which the control device predicts the harvest date of the first fruit vegetable>
Hereinafter, with reference to FIG. 9, the process in which the control device 36 predicts the predicted harvest date of the first fruit vegetable F1 will be described. FIG. 9 is a diagram illustrating an example of a process flow in which the control device 36 predicts the predicted harvest date of the first fruit vegetable F1. In the following, the imaging unit 11 has been installed at a position where the region including the first fruit vegetable F1 can be imaged, and the detection unit 12 can detect the first temperature in the growing environment of the first fruit vegetable F1 as the first environment information. The case where it has already been installed at various positions will be described. Moreover, below, the control apparatus 36 demonstrates the case where operation which starts the prediction process of the predicted harvest date of the 1st fruit vegetable F1 has been received from the user.

  The imaging unit control unit 361 causes the imaging unit 11 to capture a range that can be captured by the imaging unit 11, and the detection unit control unit 362 detects the first temperature at the position where the detection unit 12 is installed as the first environment information. 12 is detected (step S210). Then, the imaging unit control unit 361 outputs the captured image captured by the imaging unit 11 to the control device 36 as a first captured image. Further, the detection unit control unit 362 outputs the first environment information detected by the detection unit 12 to the control device 36.

  Next, the captured image acquisition unit 364 and the environment information acquisition unit 366 perform a first acquisition process (step S220). Specifically, the captured image acquisition unit 364 acquires the first captured image output from the imaging unit 11 to the control device 36 in step S110 from the imaging unit 11 in the first acquisition process. Moreover, the environment information acquisition part 366 acquires the 1st environment information which the detection part 12 output to the control apparatus 36 in step S110 from the detection part 12 in a 1st acquisition process.

  Next, the captured image acquisition unit 364 and the environment information acquisition unit 366 perform a first storage process (step S230). Specifically, the captured image acquisition unit 364 stores the first captured image acquired from the imaging unit 11 in step S220 as the first prediction data D11 in the spectrum DB 321 in the first storage process. In addition, in the first storage process, the environment information acquisition unit 366 stores the second environment information acquired from the detection unit 12 in step S220 in the environment information DB 322 as the second prediction data D21.

  Next, the spectrum generation unit 365, based on the operation received from the user, the first region corresponding to the first fruit vegetable F1 among the regions on the first captured image stored as the first prediction data D11 in the spectrum DB 321. In step S240, the specified area is specified. Here, the process of specifying the designated area in step S240 is the same as the process of specifying the designated area in step S170, and thus the description thereof is omitted.

  Next, the spectrum generation unit 365 specifies the intensities of a plurality of wavelengths associated with the respective positions in the designated region specified in step S240. The spectrum generation unit 365 calculates the average value of the intensity at each position for each of a plurality of wavelengths. The spectrum generation unit 365 generates, as a first spectral spectrum, a spectrum indicating the intensity of the plurality of wavelengths based on the average value of the intensity at each position calculated for each of the plurality of wavelengths (step S250).

  Next, the calculation unit 369 calculates the first index value based on the first spectral spectrum calculated by the spectrum generation unit 365 in step S250 (step S260). Here, the process of calculating the first index value in step S260 is the same as the process of calculating the second index value in step S190, and thus description thereof is omitted.

  Next, the prediction information correction unit 368 reads the prediction information stored in advance in the storage device 32. The prediction information correction unit 368 corrects the read prediction information based on the first temperature indicated by the first environment information stored as the second prediction data D21 in the environment information DB 322 (step S270). Here, the process of step S270 will be described.

  The prediction information correction unit 368 calculates a value obtained by dividing the average value of the second temperature associated with the read prediction information by the first temperature indicated by the information stored as the second prediction data D21 in the environment information DB 322. To do. The prediction information correction unit 368 corrects the prediction information by multiplying the calculated value by the elapsed time in the prediction information (or the number of remaining days when converted to the number of remaining days). Specifically, the first temperature is TP1, the second temperature is TP2, a value on the horizontal axis of the function representing the prediction information is X, and a value corresponding to the X on the vertical axis of the function is Y. In this case, in the corrected prediction information, Y is a value corresponding to (TP2 / TP1) × X on the vertical axis of the function. That is, when the TP1 that is the first temperature is higher than the TP2 that is the second temperature, this correction is based on the predicted harvest date of the first fruit vegetable F1 from the date and time indicated by the first date and time information associated with the first captured image. When the number of remaining days until becomes shorter and TP1 that is the first temperature is lower than TP2 that is the second temperature, it reflects that the number of remaining days from the date and time to the predicted harvest date of the first fruit vegetable F1 becomes longer. Yes.

  After the process of step S270 is performed, the harvest date prediction unit 370 converts the elapsed time in the prediction information corrected in step S270 into the number of remaining days until the harvest date of the second fruit vegetable F2 (step S280). Specifically, the harvest date prediction unit 370 converts the elapsed time into the remaining number of days by subtracting the maximum value of the elapsed time in the prediction information from the elapsed time in the prediction information to a positive value. .

  Next, the harvest date prediction unit 370 predicts the predicted harvest date of the first fruit vegetable F1 based on the first index value calculated by the calculation unit 369 in step S260 and the prediction information converted in step S280. (Step S290). Here, the process of step S290 will be described with reference to FIG.

  FIG. 10 is a diagram illustrating an example of a graph representing a temporal change in the second index value in the prediction information converted by the process of step S280. The vertical axis of the graph shows the normalized vegetation index that is the second index value. The horizontal axis of the graph indicates the number of days remaining until the harvest date of the second fruit vegetable F2. A curve FC2 shown in FIG. 10 represents the temporal change. The harvest date prediction unit 370 specifies the remaining number of days corresponding to the second index value having the same value as the first index value calculated by the calculation unit 369 in step S260 based on the prediction information. The value Y1 illustrated in FIG. 10 is an example of a second index value having the same value as the first index value calculated by the calculation unit 369 in step S260. The harvest date prediction unit 370 specifies the remaining number of days X1 that is the number of remaining days according to the value Y1, based on the prediction information, and the predicted harvest date of the first fruit vegetable F1 based on the number of remaining days X1 and today's date. Is identified (ie, predicted).

  After the process of step S290 is performed, the display control unit 363 causes the display unit of the display device 35 to display the predicted harvest date of the first fruit vegetable F1 predicted by the harvest date prediction unit 370 in step S290 (step S300). The process is terminated.

  The harvest date prediction unit 370 receives the weather prediction data including the predicted daily temperature in the period including today from now on to the future from the server storing the weather prediction data via the network NW. If it can be acquired, the predicted harvest date of the first fruit vegetable F1 may be predicted based on the predicted daily temperature during the period and the predicted information read from the storage device 32. In this case, the prediction information correction unit 368 does not correct the prediction information. In this case, the harvest date prediction unit 370 converts the elapsed time in the prediction information read from the storage device 32 into the number of remaining days until the harvest date of the second fruit vegetable F2. The harvest date prediction unit 370 specifies the remaining number of days until the harvest date of the first fruit vegetable F1 as the provisional remaining number based on the converted prediction information and the first index value calculated by the calculation unit 369 in step S260. . Since the method for specifying the provisional remaining number of days is the same as the method for specifying the number of remaining days until the harvest date of the first fruit vegetable F1 in step S290, the description thereof is omitted. Below, the case where a provisional remaining number of days is 10 days is demonstrated as an example. The harvest date prediction unit 370 calculates, as the first threshold value, a value obtained by multiplying the identified provisional remaining number of days by 10 days and the second temperature associated with the prediction information. Here, the environment information acquisition unit 366 acquires, as the first environment information, weather forecast data including the predicted temperature for each day in a period until the number of days longer than the specified provisional remaining days elapses from today. The period is, for example, a period twice the provisional remaining days (may be a period shorter than a double period or may be a period longer than a double period). In this example, 20 days. Then, the harvest date predicting unit 370 sequentially accumulates the predicted daily temperature from today's predicted temperature to today's predicted temperature after 20 days based on the first environmental information. The date exceeding the first threshold is specified. Then, the harvest date prediction unit 370 newly calculates the number of days from today until that date as the number of days remaining until the harvest date of the first fruit vegetable F1. The harvest date predicting unit 370 specifies (that is, predicts) the predicted harvest date of the first fruit vegetable F1 based on the calculated remaining number of days and today's date. Thereby, the control apparatus 36 can estimate the harvest date of the 1st fruit vegetable F1 with a higher precision than the case where the prediction information correction | amendment part 368 corrects prediction information.

  As described above, the control device 36 predicts the predicted harvest date of the first fruit vegetable F1 based on the prediction information. Accordingly, the control device 36 can accurately predict the predicted harvest date of the first fruit vegetable F1 even when the growth environment of the first fruit vegetable F1 is different from the growth environment of the second fruit vegetable F2. The control device 36 described above may be configured to acquire one or both of the first spectral spectrum and the second spectral spectrum from another device.

  As described above, the information processing apparatus 30 according to the above-described embodiment has each of a plurality of wavelengths constituting the reflected light reflected on at least a part of the surface of the first fruit vegetable (in this example, the first fruit vegetable F1). The first index value indicating the growth state of the first fruit vegetable is calculated based on the first spectral spectrum indicating the intensity of the monochromatic light, the first environment information indicating the growth environment of the first fruit vegetable, and the type of the first fruit vegetable Is a prediction information indicating the relationship between the second index value corresponding to the elapsed time and the elapsed time, the second environment indicating the growth environment of the second fruit vegetable. Based on the prediction information associated with the information and the calculated first index value, the harvest date (predicted harvest date in this example) of the first fruit vegetable is predicted. Thereby, even if the growth environment of a 1st fruit vegetable differs from the growth environment of a 2nd fruit vegetable, the harvest date of a 1st fruit vegetable can be estimated accurately.

  In the information processing apparatus 30, the prediction information is a function that represents a change in the second index value according to a change in elapsed time. Thereby, the information processing apparatus 30 is based on the case where the growth environment of the first fruit vegetable is different from the growth environment of the second fruit vegetable based on the prediction information that is a function representing the change of the second index value according to the change of the elapsed time. Even if it exists, the harvest date of a 1st fruit vegetable can be estimated accurately.

  Further, the information processing apparatus 30 predicts the harvest date of the first fruit vegetable based on the corrected prediction information and the calculated first index value. Thereby, the information processing apparatus 30 is based on the corrected prediction information and the calculated first index value, even if the growing environment of the first fruit vegetables is different from the growing environment of the second fruit vegetables. It is possible to accurately predict the harvest date.

  Further, the information processing device 30 corrects the prediction information by multiplying the elapsed time in the prediction information by a value obtained by dividing the second temperature by the first temperature. As a result, the information processing apparatus 30 determines that the growing environment of the first fruit vegetable is the second fruit vegetable based on the prediction information corrected by multiplying the elapsed time in the prediction information by the value obtained by dividing the second temperature by the first temperature. Even if it is different from the growth environment, the harvest date of the first fruit vegetable can be accurately predicted.

  Further, in the information processing apparatus 30, the first index value is a value based on the respective intensities of the first wavelength and the second wavelength, which are the two wavelengths serving as a reference among the intensities included in the first spectral spectrum, The second index value is the first wavelength and the first of the intensities included in the second spectral spectrum indicating the intensity of the monochromatic light of each of the plurality of wavelengths constituting the reflected light reflected on at least a part of the surface of the first fruit vegetable. It is a value based on the intensity of each of the two wavelengths. Thereby, the information processing apparatus 30 has a first index value that is a value based on the respective intensities of the first wavelength and the second wavelength, which are the two wavelengths serving as a reference among the intensities included in the first spectral spectrum, Each of the first wavelength and the second wavelength among the intensities included in the second spectral spectrum indicating the intensity of the monochromatic light of each of the plurality of wavelengths constituting the reflected light reflected on at least a part of the surface of the first fruit vegetable. Based on the second index value, which is a value based on the intensity, even when the growth environment of the first fruit vegetable is different from the growth environment of the second fruit vegetable, the harvest date of the first fruit vegetable can be accurately predicted. .

  In the information processing apparatus 30, the first index value is a normalized vegetation index based on the intensity of the first wavelength and the intensity of the second wavelength among the intensity included in the first spectral spectrum, and the second index value Is a normalized vegetation index based on the intensity of the first wavelength and the intensity of the second wavelength among the intensities included in the second spectral spectrum. Thereby, the information processing apparatus 30 includes the first index value that is a normalized vegetation index based on the intensity of the first wavelength and the intensity of the second wavelength among the intensity included in the first spectrum, and the second spectrum. Based on the second index value, which is a normalized vegetation index based on the intensity of the first wavelength and the intensity of the second wavelength, of the intensity included in the Even if they are different, the harvest date of the first fruit vegetable can be accurately predicted.

  Further, in the information processing apparatus 30, at least one of the first wavelength and the second wavelength is a wavelength in the near infrared region. Thereby, even if information processing apparatus 30 is a case where the growth environment of the 1st fruit vegetables differs from the growth environment of the 2nd fruit vegetables based on the 1st wavelength and the 2nd wavelength in which at least one is a near-infrared wavelength. The harvest date of the first fruit vegetables can be accurately predicted.

  Further, the information processing apparatus 30 generates a first spectral spectrum based on the first captured image captured by the imaging unit (the imaging unit 11 in this example). Thereby, the information processing apparatus 30 is a case where the growing environment of the first fruit vegetables is different from the growing environment of the second fruit vegetables based on the first spectral spectrum generated based on the first captured image captured by the imaging unit. However, the harvest date of the first fruit vegetables can be accurately predicted.

  Moreover, in the information processing apparatus 30, the imaging unit is a spectrum camera, and the first captured image is an optical spectrum image. As a result, the information processing apparatus 30 has the second fruit vegetable growing environment based on the first spectral spectrum generated based on the first captured image that is the optical spectrum image captured by the imaging unit that is a spectrum camera. Even if it is different from the growing environment of fruit vegetables, the harvest date of the first fruit vegetables can be accurately predicted.

  In addition, the information processing apparatus 30 grows the second fruit vegetable based on the second spectral spectrum indicating the intensity of the monochromatic light of each of the plurality of wavelengths constituting the reflected light reflected on at least a part of the surface of the second fruit vegetable. A second index value indicating the state and corresponding to the elapsed time is calculated, second environment information indicating the growth environment of the second fruit vegetable, and the calculated second index value corresponding to the elapsed time Based on the two index values, prediction information that is information representing the relationship between the second index value and the elapsed time and that is associated with the second environment information is generated. Thereby, the information processing apparatus 30 accurately predicts the harvest date of the first fruit vegetable based on the generated prediction information even when the growth environment of the first fruit vegetable is different from the growth environment of the second fruit vegetable. Can do.

  The embodiment of the present invention has been described in detail with reference to the drawings. However, the specific configuration is not limited to this embodiment, and changes, substitutions, deletions, and the like are possible without departing from the gist of the present invention. May be.

  Further, a program for realizing the function of an arbitrary component in the above-described apparatus (for example, information processing apparatus 30) is recorded on a computer-readable recording medium, and the program is read into a computer system and executed. You may make it do. Here, the “computer system” includes hardware such as an OS (Operating System) and peripheral devices. The “computer-readable recording medium” refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, a CD (Compact Disk) -ROM, or a storage device such as a hard disk built in the computer system. . Furthermore, “computer-readable recording medium” means a volatile memory (RAM) inside a computer system that becomes a server or client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. In addition, those holding programs for a certain period of time are also included.

In addition, the above program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line.
Further, the above program may be for realizing a part of the functions described above. Further, the program may be a so-called difference file (difference program) that can realize the above-described functions in combination with a program already recorded in the computer system.

DESCRIPTION OF SYMBOLS 1 ... Information processing system, 11 ... Imaging part, 12 ... Detection part, 30 ... Information processing apparatus, 32 ... Memory | storage device, 33 ... Input device, 35 ... Display apparatus, 36 ... Control apparatus, 321 ... Spectrum DB, 322 ... Environment Information DB, 361 ... Imaging unit control unit, 362 ... Detection unit control unit, 363 ... Display control unit, 364 ... Captured image acquisition unit, 365 ... Spectrum generation unit, 366 ... Environmental information acquisition unit, 367 ... Prediction information generation unit, 368 ... Prediction information correction unit, 369 ... Calculation unit, 370 ... Harvest date prediction unit, D11 ... First prediction data, D12 ... First prediction information generation data, D21 ... Second prediction data, D22 ... Second prediction Data for generating information, F1 ... first fruit vegetables, F2 ... second fruit vegetables, NW ... network

Claims (9)

A first index value indicating a growth state of the first fruit vegetable based on a first spectral spectrum indicating the intensity of monochromatic light of each of a plurality of wavelengths constituting the reflected light reflected on at least a part of the surface of the first fruit vegetable. A calculation unit for calculating
First environmental information indicating a growth environment of the first fruit vegetables, an index value indicating a growth state of a second fruit vegetable of the same type as the first fruit vegetables, and a second index value corresponding to an elapsed time and the progress Prediction information representing a relationship with time, based on the prediction information associated with the second environment information indicating the growth environment of the second fruit vegetable and the first index value calculated by the calculation unit A prediction unit for predicting the harvest date of the first fruit vegetables;
An information processing apparatus comprising: A correction unit that corrects the prediction information based on the first environment information;
The prediction unit predicts a harvest date of the first fruit vegetable based on the prediction information corrected by the correction unit and the first index value calculated by the calculation unit.
The information processing apparatus according to claim 1. The first environment information is a first temperature in a growth environment of the first fruit vegetables,
The second environment information is a second temperature in the growth environment of the second fruit vegetable,
The correction unit corrects the prediction information by multiplying the elapsed time in the prediction information by a value obtained by dividing the second temperature by the first temperature.
The information processing apparatus according to claim 2. The first index value is a value based on respective intensities of a first wavelength and a second wavelength, which are two wavelengths serving as a reference among the intensities included in the first spectral spectrum,
The second index value is the first of the intensities included in a second spectral spectrum that indicates the intensity of monochromatic light at each of a plurality of wavelengths constituting the reflected light reflected on at least a part of the surface of the first fruit vegetable. A value based on the intensity of each of the wavelength and the second wavelength,
The information processing apparatus according to any one of claims 1 to 3. An imaging unit capable of capturing a captured image including intensities of a plurality of wavelengths;
A spectrum generator for generating the first spectral spectrum;
With
The spectrum generation unit generates the first spectral spectrum based on a first captured image in which the imaging unit captures an area including the first fruit vegetables.
The information processing apparatus according to any one of claims 1 to 4. It is an index value indicating the growth state of the second fruit vegetable based on the second spectral spectrum indicating the intensity of monochromatic light of each of a plurality of wavelengths constituting the reflected light reflected on at least a part of the surface of the second fruit vegetable. Calculating a second index value according to the elapsed time,
Based on the second environment information indicating the growth environment of the second fruit vegetable and the second index value calculated by the calculation unit and the second index value corresponding to the elapsed time, the second index A prediction information generating unit that generates prediction information that is prediction information that is information representing a relationship between a value and the elapsed time, and is associated with the second environment information;
An information processing apparatus comprising: A first index value indicating a growth state of the first fruit vegetable based on a first spectral spectrum indicating the intensity of monochromatic light of each of a plurality of wavelengths constituting the reflected light reflected on at least a part of the surface of the first fruit vegetable. A calculating step for calculating
First environmental information indicating a growth environment of the first fruit vegetables, an index value indicating a growth state of a second fruit vegetable of the same type as the first fruit vegetables, and a second index value corresponding to an elapsed time and the progress Prediction information indicating a relationship with time, based on the prediction information associated with the second environment information indicating the growth environment of the second fruit vegetable and the first index value calculated by the calculation step Predicting the harvest date of the first fruit vegetables;
An information processing method comprising: It is an index value indicating the growth state of the second fruit vegetable based on the second spectral spectrum indicating the intensity of monochromatic light of each of a plurality of wavelengths constituting the reflected light reflected on at least a part of the surface of the second fruit vegetable. Calculating a second index value corresponding to the elapsed time,
Based on the second environment information indicating the growth environment of the second fruit vegetable and the second index value calculated by the calculation step and the second index value corresponding to the elapsed time, the second index A prediction information generation step of generating prediction information that is prediction information that is information representing a relationship between a value and the elapsed time and is associated with the second environment information;
An information processing method comprising: On the computer,
It is an index value indicating the growth state of the second fruit vegetable based on the second spectral spectrum indicating the intensity of monochromatic light of each of a plurality of wavelengths constituting the reflected light reflected on at least a part of the surface of the second fruit vegetable. A second calculation step of calculating a second index value according to the elapsed time,
Based on the second environment information indicating the growth environment of the second fruit vegetables and the second index value calculated by the second calculation step and the second index value corresponding to the elapsed time, A prediction information generation step of generating prediction information that is prediction information that is information representing a relationship between two index values and the elapsed time, and is associated with the second environment information;
Based on the first spectral spectrum indicating the intensity of monochromatic light at each of a plurality of wavelengths constituting the reflected light reflected on at least part of the surface of the first fruit vegetable of the same type as the second fruit vegetable type, A first calculation step of calculating a first index value indicating a growing state of fruit vegetables;
Based on the first environment information indicating the growth environment of the first fruit vegetables, the prediction information generated by the prediction information generation step, and the first index value calculated by the first calculation step, the first A prediction step for predicting the harvest date of one fruit vegetable;
A program for running

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