CN112931167A - Plant irrigation decision system and method - Google Patents
Plant irrigation decision system and method Download PDFInfo
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- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
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Abstract
The invention provides a plant irrigation decision-making system and method, which comprise the following steps: the device comprises a water potential instrument, a power supply device, a liquid flow meter, a data acquisition unit and a processing terminal, wherein the water potential instrument is fixed at the base end and the top end of a plant to be detected and the base end and the top end of a reference plant; the liquid flow meter is fixed on the plant to be detected and the reference plant; the water potential and the transpiration water consumption rate collected by the water potential meter and the liquid flow meter are transmitted to the processing terminal after being collected by the data collector; the power supply device supplies power to the water potential meter and the liquid flow meter. The system and the method provided by the invention can realize long-term accurate monitoring of the moisture condition of the plant under the condition of no destructive damage to the plant.
Description
Technical Field
The invention relates to the technical field of irrigation decision-making, in particular to a plant irrigation decision-making system and method.
Background
With the increasing demand for water from industrial, environmental, municipal and service industries, irrigation agriculture is under tremendous pressure to reduce water usage while maintaining high yields. One strategy currently being investigated is deficit irrigation (RDI), which achieves higher water production rates by precise, differentiated irrigation throughout the entire growth period of a crop, based on the drought sensitivity of the crop at each growth stage.
The real-time and accurate monitoring of the crop moisture condition is important for implementing RDI to guide irrigation decision and optimize an irrigation system. Current methods of obtaining crop moisture status information rely primarily on measurements of soil moisture or are based on simple methods to calculate crop water demand (e.g., based on potential evapotranspiration and crop index) or on the physiological response of plants to drought. One problem with soil-based measurements is that the plant response to soil moisture depends on many other factors, including evaporation requirements, plant root water uptake capacity, plant moisture transport capacity, crop species, and the like. Evapotranspiration estimation requires a good estimate of the crop index, which can be affected by plant density, crop growth such as changes in canopy coverage. Direct measurement of plant water status based on plant response to drought physiology is the best method to judge water deficit, as these measurements provide direct information on the apparent deficit level experienced by the plant. However, until recently, these measurements were still very laborious and non-automated, and for example, the traditional in vitro measurement process of leaf water potential and stem water potential can cause damage to plants, and it is difficult to accurately indicate the water condition of plants for a long time.
Plant flow can represent water consumption on a plant-by-plant scale, and additionally flow measurement can provide a wealth of information about water-conducting xylem function, physiology, and plant moisture status. As the xylem grows, develops and proliferates, new vessels, tracheids develop and old become dysfunctional due to gas, gel, etc., which changes increase or decrease the xylem water-conducting capacity and thus affect the liquid flow. In addition, the driving force provided by the xylem water potential gradient or the water potential difference between the roots and leaves also determines the size and direction of the flow. Therefore, by measuring the water potential gradient of the liquid flow and the driving liquid flow, the relevant information such as the state of the plant water transportation system, the hydraulic conductivity and the like which reflect the plant water condition can be obtained. Some studies have been based on flow measurements to determine the hydraulic conductivity of plants, but all have determined the water potential by using destructive pressure cell measurements, which are prone to the measured artefact by cutting the stem, and may differ significantly from the actual hydraulic conductivity of the intact plant.
Therefore, how to avoid the situation that the parameter measurement in the existing irrigation decision method causes destructive damage to the plant and cannot accurately indicate the moisture status of the plant for a long time still remains a problem to be solved by those skilled in the art.
Disclosure of Invention
The invention provides a plant irrigation decision-making system and method, which are used for solving the defects that destructive damage can be caused to plants in parameter measurement in the existing irrigation decision-making method and the moisture condition of the plants cannot be accurately indicated for a long time. The water potential meter and the liquid flow meter can not cause destructive damage to plants, and can continuously collect in real time under the condition of continuous power supply and calculate the water shortage condition in real time through the processing terminal to adjust the irrigation strategy.
The invention provides a plant irrigation decision-making system, which comprises a water potential meter, a power supply device, a liquid flow meter, a data acquisition unit and a processing terminal, wherein,
the water potential meter is fixed at the base end and the top end of the plant to be detected and the base end and the top end of the reference plant;
the liquid flow meter is fixed on the plant to be detected and the reference plant;
the water potential and the transpiration water consumption rate collected by the water potential meter and the liquid flow meter are transmitted to the processing terminal after being collected by the data collector;
the power supply device supplies power to the water potential meter and the liquid flow meter.
The plant irrigation decision-making system further comprises a repeater,
the water potential meter and the data of the plant to be detected and the reference plant collected by the liquid flow meter are sequentially transmitted to the repeater and the data collector in a wireless communication mode.
According to the plant irrigation decision-making system provided by the invention, the power supply device comprises a solar panel, a converter and a storage battery.
The invention also provides a plant irrigation decision-making method based on the plant irrigation decision-making system, which comprises the following steps:
the water potential instrument continuously collects the water potential of a first base end and the water potential of a first top end of the plant to be detected, and the water potential of a second base end and the water potential of a second top end of the reference plant in real time;
the liquid flow meter continuously collects a first transpiration water consumption rate of the plant to be detected and a second transpiration water consumption rate of the reference plant in real time;
the data acquisition unit receives the data reported by the water potential meter and the liquid flow meter and then forwards the data to the processing terminal;
the processing terminal determines the water shortage degree of the plant to be detected at the current moment based on the first base end water potential, the first top end water potential, the first transpiration water consumption rate, the second base end water potential, the second top end water potential and the second transpiration water consumption rate;
the processing terminal adjusts the irrigation strategy of the plant to be detected at the current moment based on the water shortage degree;
the reference plant and the plant to be detected have the same growth environment, type and growth stage, and the reference plant is well irrigated.
According to the plant irrigation decision-making method provided by the invention, the determining of the water shortage degree of the plant to be tested at the current time based on the first base end water potential, the first top end water potential, the first transpiration water consumption rate, the second base end water potential, the second top end water potential and the second transpiration water consumption rate specifically comprises:
determining the in-situ hydraulic conductivity of the plant to be detected based on the first base end water potential, the first top end water potential and the first transpiration water consumption rate;
determining an in-situ hydraulic conductivity of the reference plant based on the second basal end water potential, the second apical water potential, and a second transpiration water consumption rate;
determining the water conductivity index of the plant to be detected based on the in-situ water conductivity of the plant to be detected and the in-situ water conductivity of the reference plant;
and determining the water shortage degree of the plant to be detected based on the water guide index.
According to the plant irrigation decision-making method provided by the invention, the determining of the in-situ water conductivity of the plant to be tested based on the first base end water potential, the first top end water potential and the first transpiration water consumption rate specifically comprises the following steps:
determining the in-situ hydraulic conductivity K of the plant to be detected by the following formulat:
Wherein,
for the purpose of the first base end water potential,
is the first top water potential, Tr_t(ii) is the first transpiration water consumption rate;
correspondingly, the determining the in-situ hydraulic conductivity of the reference plant based on the second basal end water potential, the second apical water potential and the second transpiration water consumption rate specifically includes:
determining the in-situ hydraulic conductivity K of the reference plant by the following formular:
Wherein,
for the purpose of the second base end water potential,
is the second top water potential, Tr_rIs the second transpiration water consumption rate.
According to the plant irrigation decision-making method provided by the invention, the determining of the water conductivity index of the plant to be tested based on the in-situ water conductivity of the plant to be tested and the in-situ water conductivity of the reference plant specifically comprises the following steps:
determining the water conductivity index K of the plant to be detected by the following formularatio:
Wherein, KtIs the in-situ hydraulic conductivity, K, of the plant to be testedrAnd the in-situ hydraulic conductivity of the reference plant.
According to the plant irrigation decision-making method provided by the invention, the determination of the water shortage degree of the plant to be detected based on the water guide index specifically comprises the following steps:
and the water shortage degree of the plant to be detected is inversely related to the water guide index.
According to the plant irrigation decision-making method provided by the invention, the water shortage degree of the plant to be detected is negatively correlated with the water guide index, and the method specifically comprises the following steps:
and if the water guide index is smaller than a preset threshold value, judging that the plant to be detected is in a water shortage state, otherwise, judging that the plant to be detected is in a water shortage-free state.
According to the plant irrigation decision-making method provided by the invention, the continuous real-time collection time period is 11 to 14 points per day.
According to the plant irrigation decision-making system and method provided by the invention, the water potential meter continuously collects the water potential of the first base end and the water potential of the first top end of the plant to be detected, and the water potential of the second base end and the water potential of the second top end of the reference plant in real time; the liquid flow meter continuously collects a first transpiration water consumption rate of the plant to be detected and a second transpiration water consumption rate of the reference plant in real time; the data acquisition unit receives the data reported by the water potential meter and the liquid flow meter and then forwards the data to the processing terminal; the processing terminal determines the water shortage degree of the plant to be detected at the current moment based on the first base end water potential, the first top end water potential, the first transpiration water consumption rate, the second base end water potential, the second top end water potential and the second transpiration water consumption rate; the processing terminal adjusts the irrigation strategy of the plant to be detected at the current moment based on the water shortage degree; the reference plant and the plant to be detected have the same growth environment, type and growth stage, and the reference plant is well irrigated. The method comprises the steps of collecting the water potential and the transpiration water consumption rate of a plant by adopting a nondestructive sensor such as a water potential meter and a liquid flow meter, determining the water condition of the plant to be detected based on the collected water potential and the transpiration water consumption rate of the plant to be detected and a reference plant, and adjusting the current irrigation strategy based on the water condition. The water potential meter and the liquid flow meter can not cause destructive damage to plants, and can continuously collect in real time under the condition of continuous power supply and calculate the water shortage condition in real time through the processing terminal to adjust the irrigation strategy. Therefore, the system and the method provided by the invention can accurately monitor the moisture condition of the plant for a long time under the condition of no destructive damage to the plant.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a plant irrigation decision system according to the present invention;
FIG. 2 is a second schematic block diagram of a plant irrigation decision system according to the present invention;
FIG. 3 is a schematic flow chart of a plant irrigation decision method based on a plant irrigation decision system according to the present invention;
FIG. 4 shows the hydraulic conductivity K provided by the present inventionhWater conductivity index KratioGraph of experimental results of changes.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The existing irrigation decision-making method has the problems that destructive damage can be caused to plants by parameter measurement, and the moisture condition of the plants cannot be accurately indicated for a long time. A plant irrigation decision system of the present invention is described below in conjunction with fig. 1. Fig. 1 is a schematic physical structure diagram of a plant irrigation decision-making system provided by the present invention, as shown in fig. 1, the system includes a water potential meter, a power supply device, a liquid flow meter, a data collector and a processing terminal, wherein,
the water potential meter is fixed at the base end and the top end of the plant to be detected and the base end and the top end of the reference plant;
the liquid flow meter is fixed on the plant to be detected and the reference plant;
the water potential and the transpiration water consumption rate collected by the water potential meter and the liquid flow meter are transmitted to the processing terminal after being collected by the data collector;
the power supply device supplies power to the water potential meter and the liquid flow meter.
Specifically, the plant irrigation decision-making system provided by the invention has the advantages that the sensors directly acquiring plant moisture related parameters are water potential meters and liquid flow meters, the number of the water potential meters is at least four, one water potential meter is respectively fixed at the base end and the top end of each plant to be detected, one water potential meter is also respectively fixed at the base end and the top end of each reference plant, the number of the liquid flow meters is at least two, one reference plant is fixed at each plant to be detected, at least one plant to be detected and one reference plant are used, generally, the number of the plants to be detected is very large, the moisture condition of the plants needs to be monitored in real time due to high cost of manpower and material resources for frequent irrigation, the water content of the plants is ensured to be sufficient by irrigation at the lowest frequency, and the cost of the manpower and material resources for irrigation is. Therefore, the number of plants to be tested is usually large, that is, the number of plants to be tested, which are provided with parameter sensors, namely a water potential meter and a liquid flow meter, in an actual plant irrigation decision-making system is large, all plants to be monitored in a large-area planting area can be obtained, or a sample plant to be tested, which is obtained by sampling every preset interval in the large-area planting area, is provided with the water potential meter and the liquid flow meter to collect water related parameters, but in order to briefly explain an implementation mechanism of the plant irrigation decision-making system, data collection and analysis of one of the plants to be tested are only explained. It should be noted here that the reference plant is a plant which is in the same production environment as the plant to be tested, has the same type and has the same growth stage as the plant to be tested, specifically, N reference plants are planted simultaneously with the plant to be tested at the same soil position beside the large area where the plant to be tested is planted, where N is usually a positive integer not exceeding 3, one of the N reference plants which is closest to the growth stage of the plant to be tested is selected as the reference plant or an average value of moisture related parameters measured by the N reference plants is selected as measurement data of the reference plant, and here, for briefly explaining an implementation mechanism of the plant irrigation decision system, only data acquisition and analysis are performed by taking a condition of selecting the most suitable one of the N reference plants as the reference plant.
The water potential parameters of the plant to be tested are sent to the data acquisition unit by the first water potential meter arranged at the basic end of the plant to be tested and the second water potential meter arranged at the top end of the plant to be tested, the water potential parameters of the reference plant are sent to the data acquisition unit by the third water potential meter arranged at the basic end of the reference plant and the fourth water potential meter arranged at the top end of the reference plant, the transpiration water consumption rate of the plant to be tested is sent to the data acquisition unit by the first liquid flow meter arranged at the plant to be tested, the transpiration water consumption rate of the reference plant is sent to the data acquisition unit by the second liquid flow meter arranged at the reference plant, the data acquisition unit arranges and collects the plant water related data sent by the sensors continuously received, and forwards the plant water related data to the processing terminal, the processing terminal analyzes and calculates the water shortage condition of the plant to be. The data collector has the function of collecting measurement parameters and parameter records. The processing terminal can be a notebook computer or a tablet computer with a data processing function, and the processing terminal comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus. The processor may invoke logic instructions in the memory to perform a plant watering decision method comprising: and determining the water shortage degree of the plant to be detected at the current moment based on the first base end water potential, the first top end water potential, the first transpiration water consumption rate, the second base end water potential, the second top end water potential and the second transpiration water consumption rate, and adjusting the irrigation strategy of the plant to be detected at the current moment based on the water shortage degree.
Wherein the flow meter is used for measuring transpiration water consumption rate, namely in-situ liquid flow determination, the current commercial equipment of the flow meter generally adopts any one or a combination of three principles (a heat pulse method, a heat diffusion method and a heat balance method). The liquidometers can not influence and damage the normal physiological activities of plants, and can obtain long-term in-situ high-flux liquid flow data. The wrapped liquid flow meter is used for calculating liquid flow at a measuring position based on a heat balance method by heating plant stems, the sensor is provided with various types, such as a microsensor, a stem meter and a branch meter, which are respectively suitable for measuring the plant liquid flow of which the stem diameter is 2-5mm, 9-23mm and 32-125mm, wherein the probe with the diameter of less than 60mm has small error and high precision when applied; selecting a proper liquid flow meter according to the diameter of the stem corresponding to the type of the plant to be detected, wherein the liquid flow meter with larger size needs to be replaced at the later growth stage when the diameter of the stem changes greatly in the development process of only a few plants; the water potential meter is arranged at the base end and the top end of a plant, the water potential gradient value of the stem is obtained by calculating the difference of the two measured water potentials, the measurement equipment for determining the water potential gradient value is an in-situ stem water potential meter, and the equipment can be arranged on stems of woody and herbaceous plants and can also be used for measuring the water potentials of fruits and leaves. In addition, the in-situ stem water potential measuring instrument can integrate all the ambient environment parameters influencing the plants, such as solar radiation, temperature, humidity, wind speed and water supply, into a single continuously measurable variable (water potential), and simultaneously, the complete plant water relationship can be obtained by combining the plant liquid flow measurement.
The plant irrigation decision-making system comprises a water potential instrument, a power supply device, a liquid flow meter, a data acquisition device and a processing terminal, wherein the water potential instrument is fixed at the base end and the top end of a plant to be detected and at the base end and the top end of a reference plant; the liquid flow meter is fixed on the plant to be detected and the reference plant; the water potential and the transpiration water consumption rate collected by the water potential meter and the liquid flow meter are transmitted to the processing terminal after being collected by the data collector; the power supply device supplies power to the water potential meter and the liquid flow meter. The water potential meter and the liquid flow meter are adopted to collect plant water related parameters of the sensor which can not cause destructive damage to the plant, and continuous real-time collection and analysis are carried out, so that the purpose of accurately monitoring the plant water condition for a long time under the condition that the plant is not damaged destructively is achieved. Therefore, the system provided by the invention realizes the long-term accurate monitoring of the moisture condition of the plant under the condition of no destructive damage to the plant.
Based on the above-mentioned embodiment, the system further comprises a repeater,
the water potential meter and the data of the plant to be detected and the reference plant collected by the liquid flow meter are sequentially transmitted to the repeater and the data collector in a wireless communication mode.
Specifically, in order to reduce the hardware layout cost in the system, the moisture related parameters collected by the sensors on each plant to be detected and each reference plant are sent to the repeater in a wireless signal transmission mode, then sent to the data acquisition unit in a wireless signal transmission mode by the repeater, and finally sent to the processing terminal in a wired transmission mode by the data acquisition unit, usually the data acquisition unit and the processing terminal are located at positions close to each other, but the water potential meter and the liquid flow meter sensor are both located in a farmland where the plants are located, and the distance from the sensor to the processing terminal is large, so when the data transmission is carried out in a wireless communication mode, the repeater is required to be additionally arranged in a space between the sensor and the data acquisition unit, the signals can be timely strengthened and then emitted, and the problem that the distance between the sensor and the data acquisition unit is too far is solved, the cost of the added repeater is still lower than the cost of the equipment added for the layout construction of all running lines, fig. 2 is a second physical structure schematic diagram of the plant irrigation decision-making system provided by the invention, and as shown in fig. 2, the system can realize the transmission of long-distance wireless data by adding the repeater between the sensor water potential meter, the liquid flow meter and the data acquisition unit. A repeater is a device with data forwarding and transfer functions.
Based on the above embodiment, in the system, the power supply device includes a solar panel, a converter, and a storage battery.
In particular, the power supply device is more energy-saving and environment-friendly and has lower cost for a long time when being set in a solar energy mode. The power supply device is a set of power supply equipment at least comprising a solar panel, a converter and a storage battery under the field condition, the capacity of the storage battery is properly selected according to the power supply requirements of different equipment, and a plurality of data sensors (namely a water potential meter and a liquid flow meter) are connected in parallel and then supply power.
On the basis of the above embodiment, the present invention provides a plant irrigation decision method based on the plant irrigation decision system, which is used for solving the problems that destructive damage is caused to plants and the moisture condition of the plants cannot be accurately indicated for a long time due to parameter measurement in the existing irrigation decision method. The plant irrigation decision method of a plant irrigation decision system of the present invention is described below with reference to fig. 3. Fig. 3 is a schematic flow chart of a plant irrigation decision method based on a plant irrigation decision system according to the present invention, as shown in fig. 3, the method includes the following steps:
Specifically, install and continuously gather the moisture relevant parameter of the plant that awaits measuring and reference plant in real time at the water potential appearance and the liquid flow meter of the plant that awaits measuring and reference plant, moisture relevant parameter specifically includes: the water potential of the base end measured by a water potential meter at the base end of the plant, the water potential of the top end measured by the water potential meter at the top end of the plant and the transpiration water consumption rate measured by a liquid flow meter on the plant, and the sensor continuously collects the data in real time at a certain frequency and continuously sends the data to the data collector at the same frequency.
And step 320, the data collector receives the data reported by the water potential meter and the liquid flow meter and then forwards the data to the processing terminal.
Specifically, the data acquisition unit has a function of collecting and recording data, so that after the data acquisition unit receives the data of water potential and transpiration water consumption rate continuously sent by the water potential meter and the liquid flow meter on the plants at a certain frequency, the data acquisition unit carries out classified collection and recording on each plant, and then sends the data to the terminal processor at a self-set frequency, wherein the self-frequency is related to the data volume sent each time, and the larger the data volume transmitted each time is, the smaller the self-frequency is.
Specifically, after receiving parameters related to water, such as water potential and transpiration water consumption rate of a plant to be detected and a reference plant, sent from a data acquisition unit, a processing terminal establishes a specific mathematical model for analyzing the water shortage condition of the plant to be detected, determines the water shortage condition of the plant to be detected based on the mathematical model, and realizes timely adjustment of an irrigation strategy along with real-time monitoring of the water shortage condition of the plant to be detected. It should be noted that, as described above, the reference plant is the same as the plant to be tested in terms of growing environment, type and growing stage, and the good watering of the reference plant means that sufficient irrigation watering is performed on the reference plant in a conventional manner, because the reference plant is planted in a specific area and is small in quantity, sufficient timely and frequent irrigation watering is performed when the flooding condition of the plant does not occur, and the sufficient watering can ensure that the reference plant is in a good watering state at any time, and is more suitable for being used as the maximum reference quantity of various moisture related parameters collected under the condition of sufficient moisture. The mathematical model for analyzing the water shortage condition of the plant to be tested in the processing terminal is also constructed based on the measurement parameter of the reference plant as the maximum reference quantity, the difference value or the proportion between the measurement parameter of the plant to be tested and the maximum value measured by the corresponding reference plant can be used for measuring the water shortage condition of the plant to be tested, and the specific construction method of the mathematical model is not specifically limited here.
According to the plant irrigation decision-making method provided by the invention, the water potential meter continuously collects the water potential of the first base end and the water potential of the first top end of the plant to be detected, and the water potential of the second base end and the water potential of the second top end of the reference plant in real time; the liquid flow meter continuously collects a first transpiration water consumption rate of the plant to be detected and a second transpiration water consumption rate of the reference plant in real time; the data acquisition unit receives the data reported by the water potential meter and the liquid flow meter and then forwards the data to the processing terminal; the processing terminal determines the water shortage degree of the plant to be detected at the current moment based on the first base end water potential, the first top end water potential, the first transpiration water consumption rate, the second base end water potential, the second top end water potential and the second transpiration water consumption rate; the processing terminal adjusts the irrigation strategy of the plant to be detected at the current moment based on the water shortage degree; the reference plant and the plant to be detected have the same growth environment, type and growth stage, and the reference plant is well irrigated. The method comprises the steps of collecting the water potential and the transpiration water consumption rate of a plant by adopting a nondestructive sensor such as a water potential meter and a liquid flow meter, determining the water condition of the plant to be detected based on the collected water potential and the transpiration water consumption rate of the plant to be detected and a reference plant, and adjusting the current irrigation strategy based on the water condition. The water potential meter and the liquid flow meter can not cause destructive damage to plants, and can continuously collect in real time under the condition of continuous power supply and calculate the water shortage condition in real time through the processing terminal to adjust the irrigation strategy. Therefore, the method provided by the invention realizes the long-term accurate monitoring of the moisture condition of the plant under the condition of no destructive damage to the plant.
Based on the above embodiment, in the method, the determining the water shortage degree of the plant to be tested at the current time based on the first base end water potential, the first top end water potential, the first transpiration water consumption rate, the second base end water potential, the second top end water potential, and the second transpiration water consumption rate specifically includes:
determining the in-situ hydraulic conductivity of the plant to be detected based on the first base end water potential, the first top end water potential and the first transpiration water consumption rate;
determining an in-situ hydraulic conductivity of the reference plant based on the second basal end water potential, the second apical water potential, and a second transpiration water consumption rate;
determining the water conductivity index of the plant to be detected based on the in-situ water conductivity of the plant to be detected and the in-situ water conductivity of the reference plant;
and determining the water shortage degree of the plant to be detected based on the water guide index.
Specifically, the analysis and calculation modes of the processing terminal after receiving the respective water related parameters of the plant to be detected and the reference plant collected by the sensor are further limited. Firstly, the physical parameter for representing the water shortage condition of the plant is determined to be the in-situ hydraulic conductivity, the in-situ hydraulic conductivity is positively correlated with the transpiration water consumption rate of the plant, but is negatively correlated with the water potential gradient of the plant, therefore, a calculation formula of the in-situ hydraulic conductivity can be constructed through the positive and negative correlation among the in-situ hydraulic conductivity, the transpiration water consumption rate and the water potential gradient, and the mathematical formula of the positive correlation and the negative correlation can be constructed in various ways, such as a simplest linear proportion method, a more complex parameter fitting method and the like, and the time is not limited in detail. It should be noted here that, if the in-situ water conductivity of the plant to be tested is determined according to the preset rule based on the first base end water potential, the first top end water potential and the first transpiration water consumption rate, the in-situ water conductivity of the reference plant needs to be determined according to the same preset rule based on the second base end water potential, the second top end water potential and the second transpiration water consumption rate, so that the in-situ water conductivity of the plant to be tested and the reference plant determined according to the same rule can be compared or analyzed and calculated by using the in-situ water conductivity of the reference plant as a reference value to obtain the water shortage condition of the plant to be tested. Still further, the water conductivity index of the plant to be tested is determined based on the in-situ water conductivity of the plant to be tested and the in-situ water conductivity of the reference plant, and there may be a plurality of ways to construct the water conductivity index, such as simply the water conductivity index being the in-situ water conductivity of the plant to be tested/the in-situ water conductivity of the reference plant, or the water conductivity index being the in-situ water conductivity of the reference plant-the in-situ water conductivity of the plant to be tested/the in-situ water conductivity of the reference plant, and so on, which is not limited herein. And judging the water shortage condition of the plant to be detected according to the water guide index, and adjusting the irrigation strategy in real time according to the water shortage condition, for example, if serious water shortage is judged, starting an automatic water spraying gun to spray and irrigate with large flow, and if slight water shortage is judged, starting the automatic water spraying gun to spray and irrigate with small flow.
Based on the above embodiment, in the method, determining the in-situ water conductivity of the plant to be tested based on the first base end water potential, the first top end water potential and the first transpiration water consumption rate specifically includes:
determining the in-situ hydraulic conductivity K of the plant to be detected by the following formulat:
Wherein,
for the purpose of the first base end water potential,
is the first top water potential, Tr_t(ii) is the first transpiration water consumption rate;
correspondingly, the determining the in-situ hydraulic conductivity of the reference plant based on the second basal end water potential, the second apical water potential and the second transpiration water consumption rate specifically includes:
determining the in-situ hydraulic conductivity K of the reference plant by the following formular:
Wherein,
for the purpose of the second base end water potential,
is the second top water potential, Tr_rIs the second transpiration water consumption rate.
Specifically, the calculation formula for constructing the in-situ hydraulic conductivity through the positive and negative correlation among the in-situ hydraulic conductivity, the transpiration water consumption rate and the water potential gradient is further defined as a simplest linear proportion method, and the derivation process is as follows:
determining the in-situ hydraulic conductivity K of the plant to be detected by the following formulat：
In the above formula
The real-time water potential of the basic end of the plant stem to be detected is expressed in MPa;
is the real-time water potential of the top end of the plant stem to be detected, and has the unit of MPa and Tr_tIs the real-time transpiration water consumption rate of the plant per leaf area, and the unit is mm h-1And L is the path length between two water potential measurement points, namely the length of the water potential gradient.
In the above formula, assuming that the plant height change is not large during the research period (anagen-prophase of reproductive growth) and the distance between the water potential measuring points is relatively stable, the length L of the water potential gradient can be assumed as a constant. Based on this assumption, in analysis KtWhen time varies, L in the formula can be ignored, and then K is obtainedtIn simplified form:
the method needs a reference plant which is in the same growing environment and the same type as the plant to be detected, and the water potentials of the liquid flow of the plant to be detected and the reference plant and the water potentials of the base end and the top end of the in-situ stem are all measured in real time so as to obtain the real-time water potential gradient of the driving liquid flow.
In order to obtain a reference value for analyzing and comparing the in-situ hydraulic conductivity of the reference plant and the in-situ hydraulic conductivity of the plant to be detected, which are calculated subsequently, the in-situ hydraulic conductivity of the reference plant also needs to use the same calculation formula as the in-situ hydraulic conductivity of the plant to be detected, so that:
determining the in-situ hydraulic conductivity K of the reference plant by the following formular:
Wherein,
for the purpose of the first base end water potential,
is the first top water potential, Tr_rIs the first transpiration water consumption rate.
The calculation of the hydraulic conductance of the plant is carried out through a simple data model, so that the calculation speed can be increased, and the detection of the water shortage condition of the plant to be detected can be displayed in real time with better effect.
Based on the above embodiment, in the method, determining the water conductivity index of the plant to be tested based on the in-situ water conductivity of the plant to be tested and the in-situ water conductivity of the reference plant specifically includes:
determining the water conductivity index K of the plant to be detected by the following formularatio:
Wherein, KtIs the in-situ hydraulic conductivity, K, of the plant to be testedrAnd the in-situ hydraulic conductivity of the reference plant.
Specifically, the calculation formula for determining the water conductivity index of the plant to be tested through the water conductivity of the plant to be tested and the water conductivity of the reference plant is further defined as the simplest way of constructing the water conductivity index, wherein the water conductivity index is the in-situ water conductivity of the plant to be tested/the in-situ water conductivity of the reference plant. The reference plants are fully supplied with water and are in a moisture-free deficit state.
When the plant to be detected is irrigated well, the water potential gradient is small when water is not lacked, the plant transpiration water consumption intensity is high, the hydraulic conductivity reaches the peak value, and K is obtained at the momenttAnd KrSame, KratioThe result of the calculation was 1.0,namely indicating that the plant to be detected is not lack of water. When the plant transpiration to be detected completely stops, the water potential gradient is large when the plant is extremely lack of water, the plant transpiration water consumption intensity is small, the hydraulic conductivity is reduced to the minimum, and KtIs 0, KratioThe calculation result is 0, which indicates that the plant to be detected suffers the largest water deficit. The method is similar to the water deficit index of the traditional CWSI crops and the like, but has more physiological significance; comprehensively considers the comprehensive influence of various physiological and environmental factors including plant liquid flow, stem water potential and the like, and KratioThe calculation result is in accordance with [0-1.0 ]]The index of (2) has obvious application advantages. KratioCan comprehensively indicate the moisture state and the transmission capacity of plants, the result numerical value range is between 0 and 1.0, KratioThe smaller the value is, the larger the water shortage degree of the plant to be detected is.
The calculation of the water conductivity index of the plant is carried out through a simple data model, so that the calculation speed can be further increased, and the detection of the water shortage condition of the plant to be detected can be further displayed in real time with better effect.
Based on the above embodiment, in the method, the determining the water shortage degree of the plant to be tested based on the water guide index specifically includes:
and the water shortage degree of the plant to be detected is inversely related to the water guide index.
Specifically, the higher the water conductance index is, the closer the water conductance of the plant to be measured is to the water conductance of the reference plant, the less the plant to be measured is short of water shortage, the lower the water conductance index is, the farther the difference between the water conductance of the plant to be measured and the water conductance value of the reference plant is, and the more the plant to be measured is short of water shortage, so that the water shortage of the plant to be measured is negatively correlated with the water conductance index, and after a negatively correlated relationship is obtained, a determination can be made on a manner of indicating real-time adjustment of the irrigation strategy by using the water conductance index through the relationship, for example, whether irrigation is set according to a numerical value to be started or not and flow rate after the irrigation is set according to the numerical value, whether irrigation is judged according to a numerical value to be above or below a preset threshold value to be relatively rough, corresponding spray control of starting or closing the irrigation system is made, and the.
Based on the above embodiment, in the method, the water shortage degree of the plant to be tested is negatively correlated with the water guide index, and the method specifically includes:
and if the water guide index is smaller than a preset threshold value, judging that the plant to be detected is in a water shortage state, otherwise, judging that the plant to be detected is in a water shortage-free state.
Specifically, a determination method for making a manner of indicating real-time adjustment of the irrigation strategy by using the water guide index is further defined, that is, the simplest threshold determination method is, for example, a preset threshold is set to 0.5, and if the water guide index is smaller than 0.5, it is determined that the plant to be detected is in a water shortage state, otherwise, it is determined that the plant to be detected is in a water shortage state.
The water use guide index indication real-time irrigation strategy adjustment mode is formulated in a simple mode, so that the calculation speed can be further increased, and the water shortage condition detection of the plant to be detected is further displayed in a better effect in real time.
Based on the above embodiment, in the method, the time period of the continuous real-time acquisition is 11 to 14 points per day.
Specifically, it is recommended that the transpiration rate per leaf area T obtained by the flow-averaging conversion at noon time of day (e.g., 11:00-14:00) be usedr_tMean value, real-time water potential difference between the basal end and the apical end of the plant stem
Mean value, determining hydraulic conductivity, and calculating to obtain water conductivity index KratioThis is because the water potential reaches a more stable state at noon, when assuming that there is no water flow into or out of the storage zone between the xylem vessels and the xylem and bark living cells, the effect of the storage effect on the study of the change in hydraulic conductivity over time can be neglected. In addition, the plant in the period has the strongest transpiration effect, the plant is most likely to have water deficiency, and the plant in-situ hydraulic conductivity and water conductivity index K are obtained at the timeratioThe moisture condition information of the plants can be accurately reflected. Meanwhile, for higher efficiency and energy saving, the plant irrigation decision-making system provided by the invention is used for monitoring and adjusting the maximum day time interval from 11 to 14 noon sunshine every dayAnd the transpiration effect in other time periods is not obvious, and the water shortage probability is low, so that the whole day monitoring can be focused on the monitoring in the midday time period, the working time of the system is shortened, and the running resources are saved.
Based on the above embodiments, the present invention provides a plant irrigation decision system and method irrigation decision system, which integrates plant moisture status information monitoring equipment 01 (i.e. liquid flow measurement equipment, in-situ stem water potential measurement equipment), a power supply device 02, a data Repeater (Repeater) and collector 03, and a terminal processor 04.
The power supply device 02 at least comprises a set of power supply equipment consisting of a solar panel, a converter and a storage battery under the field condition, the appropriate capacity of the storage battery is selected according to the power supply requirements of different equipment, and a plurality of data measuring equipment are connected in parallel to supply power; the data relay and collector 03 is a data collector with data recording function, such as a repeater with data forwarding and transmitting function; the terminal processor 04 may be a notebook computer or a tablet computer with a data processing function.
The plant water condition information monitoring device, the data relay and collector and the terminal processor are directly electrically connected or are in intelligent and rapid wireless connection, so that data transmission or interaction is realized.
Based on the accurate intelligent irrigation decision-making system based on the in-situ high water diversion index provided by the embodiment, the following steps are executed:
and step S1, respectively acquiring real-time liquid flow and leaf area data of the reference plant and the plant to be detected, and respectively acquiring transpiration rates of the unit leaf areas of the reference plant and the plant to be detected after conversion.
In this example, the Flow data was collected from V10 in the late vegetative growth stage of maize, when the plant stems grew and developed to the minimum diameter required by the sensor, and the Flow measurement device type was a wrapped Flow meter (Flow32-1K, Dynamax inc., Houston, TX, USA), where there was still some difference in diameter for maize, so the sensor type was selected from SGEX-19 and SGEX-25, and the device collection values were set every 1 minute and recorded as an average of 15 minutes.
Alternatively, different types of packaged liquid flow meters can be selected for different plants: the microsensor, the stem meter and the branch meter are respectively suitable for measuring plant liquid flows with the stem diameters of 2-5mm, 9-23mm and 32-125mm, wherein the error is small when the probe with the diameter of less than 60mm is applied.
In the present embodiment, step S1 further includes two substeps S11 and S12,
and step S11, measuring the leaf areas of the reference plant and the plant to be measured at the beginning and the end of the key growth period, and interpolating to obtain the daily change of the leaf areas in each growth period. This can reduce the workload of the leaf area measurement while ensuring the stability of the data.
And step S12, converting the plant liquid flow in the growth period into the plant leaf area transpiration rate according to the leaf area and the water density.
And step S2, respectively acquiring real-time water potential data of the base end and the top end of the reference plant and the plant to be detected in situ stem to obtain the real-time water potential gradient of the driving liquid flow.
In this example, the in situ stem water potential real-time measurement instrument is a stem water potential meter and a PSY1 data recorder (ICT International Pty Ltd., Armidale, NSW, Australia), which are installed and maintained according to the installation procedure outlined by Tran et al. During real-time monitoring, if the water potential reading suddenly starts to zero during the day, indicating that the chamber of the stem water potential meter may be saturated with water or the thermocouple damaged, reinstallation may be necessary.
Optionally, the stem water potential meter can be installed on woody and herbaceous plant stems, and can also be used for measuring the water potential of fruits and leaves.
Step S3, respectively calculating the in-situ hydraulic conductivity K of the reference plant and the plant to be detected based on the indexestIndex K with waterratio：
Wherein,
the real-time water potential of the basic end of the plant stem is in MPa;
is the real-time water potential of the top end of the plant stem, and has the unit of MPa and Tr_tIs the real-time transpiration water consumption rate of the plant per leaf area, and the unit is mm h-1。
Wherein, KtThe in-situ hydraulic conductivity of the plant to be detected is measured in mm h-1MPa-1，KrIs the in-situ hydraulic conductivity of the reference plant in mm h-1MPa-1。
When the plant to be detected is irrigated well, the water potential gradient is small when water is not lacked, the plant transpiration water consumption intensity is high, the hydraulic conductivity reaches the peak value, and K is obtained at the momenttAnd KrSame, KratioThe calculation result is 1.0, namely the plant to be detected is indicated not to suffer from water deficit. When the plant transpiration to be detected completely stops, the water potential gradient is large when the plant is extremely lack of water, the plant transpiration water consumption intensity is small, the hydraulic conductivity is reduced to the minimum, and KtIs 0, KratioThe calculation result is 0, which indicates that the plant to be detected suffers the largest water deficit. The method is similar to the water deficit index of the traditional CWSI crops and the like, but has more physiological significance; comprehensively considers the comprehensive influence of various physiological and environmental factors including plant liquid flow, stem water potential and the like, and KratioThe calculation result is in accordance with [0-1.0 ]]The index of (2) has obvious application advantages. KratioCan comprehensively indicate the moisture state and the transmission capacity of plants, the result numerical value range is between 0 and 1.0, KratioThe smaller the value is, the larger the water shortage degree of the plant to be detected is.
The realization of the method requires that the plant to be detected and the reference plant are observed at the same time, are in the same growing environment and have the same type. The plant that awaits measuring is for transferring the deficit irrigation treatment, is in there is water deficit state, refers to the plant and is for abundant irrigation treatment, is in no water deficit state.
Preferably, the method recommends using a transpiration rate per leaf area T obtained by mean conversion of the stream during noon hours of the day (e.g. 11:00-14:00)r_tMean value, real-time water potential difference between the basal end and the apical end of the plant stem
Mean value, determining hydraulic conductivity, and calculating to obtain water conductivity index KratioThis is because the water potential reaches a more stable state at noon, when assuming that there is no water flow into or out of the storage zone between the xylem vessels and the xylem and bark living cells, the effect of the storage effect on the study of the change in hydraulic conductivity over time can be neglected. In addition, the plant in the period has the strongest transpiration effect, the plant is most likely to have water deficiency, and the plant in-situ hydraulic conductivity and water conductivity index K are obtained at the timeratioThe moisture condition information of the plants can be accurately reflected.
Step S4, according to the in-situ hydraulic conductivity KtIndex K with waterratioAnd analyzing the change of the plant water content with the growth period to obtain the dynamic information of the plant water content.
In this embodiment, FIG. 4 shows the hydraulic conductivity K provided by the present inventionhWater conductivity index KratioThe graph of the experimental results of the changes, as shown in FIG. 4, establishes the hydraulic conductivity K in the key growth period (including the late vegetative growth period (V10-VT), the early reproductive growth period (R1-R3) and the late reproductive growth period (R4-R6))hWater guide index KratioDaily change. In the later period of vegetative growth, corn is subjected to beneficial water deficiency treatment, the water irrigation amount is 65% of ET, the treated Kratio is at a lower level and is between 0.75 and 0.81, the average value of Kratio is 0.78, the Kratio of the corn is less than 0.82 in the period, and the corn is subjected to a certain degree of water deficiency. In the early stage of reproductive growth, the corn is rehydrated in a moisture sensitive stage, the water irrigation amount is 100 percent of that of fully irrigating ET, and K is shownratioElevated to a higher level of 0.85-0.93, mean 0.88, corn K at this stageratioAre all greater than 0.82, at this timeCan be relieved by water deficiency. In the later stage of reproductive growth, water deficiency treatment is carried out again, the irrigation amount is 80% of that of full irrigation ET, and at the moment, the water content deficiency index K isratioDown to 0.74-0.82 with a mean value of 0.77, phase KratioAre all less than 0.82, i.e. KratioIndicating that the corn is again subjected to a water deficit. Although the irrigation amount of the second water deficit treatment is larger, the water condition of the corn is not much different from that of the corn after the water deficit treatment in the later period of vegetative growth, which is probably related to the gradual aging of corn plants in the later period of growth and the weakening of the water absorption and transportation capacity of the corn plants. The test shows that the threshold value K of the water conductivity indexcritical0.82 corresponds well to the criticality of division of the maize growth period for which the deficit irrigation is applied. Maintaining K during the water loss of corn during the non-sensitive stageratioNot exceeding KcriticalMaintaining K when corn is rehydrated in the moisture sensitive stageratioNot less than KcriticalTherefore, according to the change of the plant in-situ water conductivity and the water conductivity Kratio along with the growth period, the dynamic information of the plant water condition can be obtained, and further, the accurate irrigation decision is helped.
The method relates to the fact that the starting time of the in-situ measurement of the flow and the stem water potential is related to the growth and development of plants. The liquid flow in-situ measurement is that when the growth and development of plant stems reach the minimum diameter required by the sensor (related to the type of the sensor), the measurement equipment can be installed to start monitoring, and the liquid flow measurement coverage time is longer. The requirement of the stem water potential in-situ measurement on the plant stem is relatively low, the stem water potential is measured in real time when the plant stem grows to be capable of being provided with equipment, and in addition, the position of a stem water potential in-situ measurement point is kept relatively fixed, so that even on the premise that the plant height changes along with time, the change rule of the plant height along with the growth period is additionally considered. For corn, the in-situ measurement time of the water potential of the liquid flow and the stem substantially covers the late vegetative growth stage (V6-VT), the early reproductive growth stage (R1-R3) and the late reproductive growth stage (R4-R6). Therefore, the water guide index Kratio of the method can realize in-situ long-term monitoring of the water condition of the plant in the key growth period, diagnose the water shortage degree of the plant according to the obtained plant water condition information, and make a differentiated strategy according to different water requirements of the plant in each growth period, so as to guide a production operator to carry out accurate irrigation decision, thereby achieving the optimal balance of irrigation water and yield.
The method relates to a system integrating a plurality of data measuring devices. The plant liquid flow can be used as an independent sensor for wireless transmission, and the synchronous determination and transmission with the plant stem water potential can be realized by adjusting the measurement and recording time, so that an integrated intelligent system of the plant physiological data determination equipment is formed. The data in the system can be transmitted through a wireless network and a Repeater (Repeater) and then linked to a local computer; the data acquisition device can be used for acquiring and storing data so as to check the data in real time and carry out intelligent analysis and processing on the data through direct electrical connection to the terminal equipment. Obtaining the water conductivity index K through calculationratioAnd the plant moisture condition information is acquired, and intelligent and accurate irrigation decision and management are realized.
According to the scheme of the invention, the accurate intelligent irrigation decision method and the system based on the in-situ high-flux water conductivity index measure the stem water potential of the basal end and the apical end of the plant in real time through in-situ high-flux and combination with the plant liquid flow measured in the flow path or the water potential gradient to obtain the physiological phenotype parameters of the plant, determine the in-situ water conductivity of the plant, and calculate the output water conductivity index KratioThe method overcomes the defects that the operation is complex, the damage to the plant is easy to generate, the time lag exists in point value measurement and discontinuous measurement in the growth period exists in the process of obtaining the plant moisture condition information based on the traditional measurement of physiological indexes such as stem water potential, leaf water potential and the like, and meanwhile, the information is easily fed back to a grower or a farmer in real time through an application program of a smart phone and a tablet personal computer, so that the grower or the farmer is helped to make a more reasonable irrigation system, the accurate intelligent irrigation decision based on the in-situ water conductivity index is realized, the optimal balance between irrigation water and yield and income is achieved, and a certain reference is provided for improving the crop moisture productivity and the agricultural.
The above-described system embodiments are merely illustrative, wherein the apparatuses or units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A plant irrigation decision-making system is characterized by comprising a water potential meter, a power supply device, a liquid flow meter, a data acquisition unit and a processing terminal, wherein,
the water potential meter is fixed at the base end and the top end of the plant to be detected and the base end and the top end of the reference plant;
the liquid flow meter is fixed on the plant to be detected and the reference plant;
the water potential and the transpiration water consumption rate collected by the water potential meter and the liquid flow meter are transmitted to the processing terminal after being collected by the data collector;
the power supply device supplies power to the water potential meter and the liquid flow meter.
2. The plant irrigation decision system of claim 1, further comprising a repeater,
the water potential meter and the data of the plant to be detected and the reference plant collected by the liquid flow meter are sequentially transmitted to the repeater and the data collector in a wireless communication mode.
3. The plant irrigation decision system of claim 1, wherein the power supply comprises a solar panel, a converter, and a battery.
4. A plant irrigation decision method based on the plant irrigation decision system of any one of claims 1-3, comprising:
the water potential instrument continuously collects the water potential of a first base end and the water potential of a first top end of the plant to be detected, and the water potential of a second base end and the water potential of a second top end of the reference plant in real time;
the liquid flow meter continuously collects a first transpiration water consumption rate of the plant to be detected and a second transpiration water consumption rate of the reference plant in real time;
the data acquisition unit receives the data reported by the water potential meter and the liquid flow meter and then forwards the data to the processing terminal;
the processing terminal determines the water shortage degree of the plant to be detected at the current moment based on the first base end water potential, the first top end water potential, the first transpiration water consumption rate, the second base end water potential, the second top end water potential and the second transpiration water consumption rate;
the processing terminal adjusts the irrigation strategy of the plant to be detected at the current moment based on the water shortage degree;
the reference plant and the plant to be detected have the same growth environment, type and growth stage, and the reference plant is well irrigated.
5. The plant irrigation decision-making method according to claim 4, wherein the determining the water shortage degree of the plant to be tested at the current time based on the first basal end water potential, the first top end water potential, the first transpiration water consumption rate, the second basal end water potential, the second top end water potential and the second transpiration water consumption rate specifically comprises:
determining the in-situ hydraulic conductivity of the plant to be detected based on the first base end water potential, the first top end water potential and the first transpiration water consumption rate;
determining an in-situ hydraulic conductivity of the reference plant based on the second basal end water potential, the second apical water potential, and a second transpiration water consumption rate;
determining the water conductivity index of the plant to be detected based on the in-situ water conductivity of the plant to be detected and the in-situ water conductivity of the reference plant;
and determining the water shortage degree of the plant to be detected based on the water guide index.
6. The plant irrigation decision-making method according to claim 5, wherein the determining the in-situ water conductivity of the plant to be tested based on the first basal end water potential, the first apical water potential and the first transpiration water consumption rate comprises:
determining the in-situ hydraulic conductivity K of the plant to be detected by the following formulat:
Wherein,
for the purpose of the first base end water potential,
is the first top water potential, Tr_t(ii) is the first transpiration water consumption rate;
correspondingly, the determining the in-situ hydraulic conductivity of the reference plant based on the second basal end water potential, the second apical water potential and the second transpiration water consumption rate specifically includes:
determining the in-situ hydraulic conductivity K of the reference plant by the following formular:
Wherein,
for the purpose of the second base end water potential,
is the second top water potential, Tr_rIs the second transpiration water consumption rate.
7. The plant irrigation decision method according to claim 5, wherein the determining the water conductivity index of the plant to be tested based on the in-situ water conductivity of the plant to be tested and the in-situ water conductivity of the reference plant comprises:
determining the water conductivity index K of the plant to be detected by the following formularatio:
Wherein, KtIs the in-situ hydraulic conductivity, K, of the plant to be testedrAnd the in-situ hydraulic conductivity of the reference plant.
8. The plant irrigation decision-making method according to claim 7, wherein the determining the water shortage degree of the plant to be tested based on the water guide index specifically comprises:
and the water shortage degree of the plant to be detected is inversely related to the water guide index.
9. The plant irrigation decision method according to claim 8, wherein the water shortage of the plant to be tested is inversely related to the water conductance index, and specifically comprises:
and if the water guide index is smaller than a preset threshold value, judging that the plant to be detected is in a water shortage state, otherwise, judging that the plant to be detected is in a water shortage-free state.
10. The method of claim 4, wherein the continuous real-time collection is from 11 to 14 points per day.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114383666A (en) * | 2022-01-20 | 2022-04-22 | 信阳师范学院 | Leaf hydraulic vulnerability curve measuring equipment and method |
| CN114441712A (en) * | 2022-01-21 | 2022-05-06 | 中国农业大学 | Irrigation curve determination method and device based on air hole fragility characteristics |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050121536A1 (en) * | 2003-11-06 | 2005-06-09 | Bavel Michael V. | Integrated sap flow monitoring, data logging, automatic irrigation control scheduling system |
| CN101836562A (en) * | 2010-05-25 | 2010-09-22 | 中国农业科学院农田灌溉研究所 | Index method for diagnosing degree of water shortage of crop |
| CN105052576A (en) * | 2015-08-19 | 2015-11-18 | 南京信息工程大学 | Determination method of drought stress level of greenhouse crops |
| CN205562511U (en) * | 2016-04-09 | 2016-09-07 | 西北农林科技大学 | Herbaceous plant transpiration rate apparatus |
| CN107945042A (en) * | 2017-11-29 | 2018-04-20 | 上海华维节水灌溉股份有限公司 | A kind of plant growth irrigation decision control system |
| CN108645892A (en) * | 2018-05-09 | 2018-10-12 | 中国农业大学 | A kind of axis flow of water measuring device and method |
| CN109425703A (en) * | 2017-08-24 | 2019-03-05 | 孟兆江 | A method of the non-destructive monitoring chamber crop water regime for Precision Irrigation control |
| CN109752488A (en) * | 2018-12-14 | 2019-05-14 | 陕西迈拓克能源科技有限公司 | A kind of portable plant flow of water tester and its application method |
| CN110320247A (en) * | 2019-07-09 | 2019-10-11 | 中国农业大学 | Plant haulm water content damage-free measuring apparatus and method |
| CN111280019A (en) * | 2020-02-06 | 2020-06-16 | 山东农业大学 | Soil moisture digital prediction and irrigation early warning method |
-
2021
- 2021-03-22 CN CN202110303094.XA patent/CN112931167B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050121536A1 (en) * | 2003-11-06 | 2005-06-09 | Bavel Michael V. | Integrated sap flow monitoring, data logging, automatic irrigation control scheduling system |
| CN101836562A (en) * | 2010-05-25 | 2010-09-22 | 中国农业科学院农田灌溉研究所 | Index method for diagnosing degree of water shortage of crop |
| CN105052576A (en) * | 2015-08-19 | 2015-11-18 | 南京信息工程大学 | Determination method of drought stress level of greenhouse crops |
| CN205562511U (en) * | 2016-04-09 | 2016-09-07 | 西北农林科技大学 | Herbaceous plant transpiration rate apparatus |
| CN109425703A (en) * | 2017-08-24 | 2019-03-05 | 孟兆江 | A method of the non-destructive monitoring chamber crop water regime for Precision Irrigation control |
| CN107945042A (en) * | 2017-11-29 | 2018-04-20 | 上海华维节水灌溉股份有限公司 | A kind of plant growth irrigation decision control system |
| CN108645892A (en) * | 2018-05-09 | 2018-10-12 | 中国农业大学 | A kind of axis flow of water measuring device and method |
| CN109752488A (en) * | 2018-12-14 | 2019-05-14 | 陕西迈拓克能源科技有限公司 | A kind of portable plant flow of water tester and its application method |
| CN110320247A (en) * | 2019-07-09 | 2019-10-11 | 中国农业大学 | Plant haulm water content damage-free measuring apparatus and method |
| CN111280019A (en) * | 2020-02-06 | 2020-06-16 | 山东农业大学 | Soil moisture digital prediction and irrigation early warning method |
Non-Patent Citations (1)
| Title |
|---|
| 杨睿智等: "基于液流和叶片水势测定的沙柳水分利用特征分析", 《北京林业大学学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114383666A (en) * | 2022-01-20 | 2022-04-22 | 信阳师范学院 | Leaf hydraulic vulnerability curve measuring equipment and method |
| CN114383666B (en) * | 2022-01-20 | 2023-11-28 | 信阳师范学院 | Leaf hydraulic vulnerability curve measuring equipment and method |
| CN114441712A (en) * | 2022-01-21 | 2022-05-06 | 中国农业大学 | Irrigation curve determination method and device based on air hole fragility characteristics |
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|---|---|
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