CN114117905A - Greenhouse crop irrigation method based on deep neural network - Google Patents
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- A—HUMAN NECESSITIES
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- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G9/00—Cultivation in receptacles, forcing-frames or greenhouses; Edging for beds, lawn or the like
- A01G9/24—Devices or systems for heating, ventilating, regulating temperature, illuminating, or watering, in greenhouses, forcing-frames, or the like
- A01G9/247—Watering arrangements
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- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
- Y02A40/25—Greenhouse technology, e.g. cooling systems therefor
Abstract
The invention provides a greenhouse crop irrigation method based on a deep neural network, which comprises the steps of firstly, constructing a crop transpiration rate prediction model, a greenhouse environment parameter prediction model, a crop moisture state detection model and a crop moisture stress recovery time prediction model based on the deep neural network; then inputting the predicted values of the crop planting date, the current time and the greenhouse environment parameter into a crop transpiration rate prediction model to obtain a crop transpiration rate predicted value, and integrating the time in the interval of the next irrigation time to obtain a crop transpiration amount predicted value; inputting the planting date, the current time, the greenhouse environment parameters, the RGB image of the crop canopy leaf, the depth image and the near-infrared image into a crop moisture state detection model to judge the moisture state of the crop, and irrigating according to the moisture state of the crop and the predicted value of the crop transpiration amount. The invention can dynamically adjust the irrigation time and the irrigation quantity, realize the accurate irrigation of crops and save the irrigation water.
Description
Technical Field
The invention belongs to the technical field of greenhouse crop irrigation, and particularly relates to a greenhouse crop irrigation method based on a deep neural network.
Background
Irrigation is the only source of moisture for greenhouse crops. Too much and too little water supply can affect the yield and quality of the crops to some extent. The root system of crops can be damaged due to excessive water supply, and the pest and disease damage and water waste are caused due to overhigh air humidity in the greenhouse; too little water supply will reduce the photosynthetic rate of the crop, resulting in reduced yield. The water demand of crops in different growth stages varies every day. The water demand of crops is small in the seedling stage, the water demand of crops is gradually increased along with the growth of the crops, and the water demand is reduced in the harvest period for controlling the quality. The changes of the temperature, the humidity and the illumination in the greenhouse at various times in a day also affect the water demand of crops, and the water demand of the crops is increased under the conditions of high temperature and high illumination intensity; the water demand of crops is reduced under the conditions of low temperature and low illumination.
Chinese patent (CN106258855A) discloses an intelligent irrigation system based on light radiation, which performs quantitative irrigation when the cumulative energy of the light radiation is greater than a predetermined critical energy. In 2018, hucho and the like, a sunlight greenhouse real-time accurate irrigation decision based on evapotranspiration and water balance is disclosed in agricultural engineering journal 34, 23, and irrigation is triggered when the total field evaporation amount is larger than the available water for crops in soil by taking days as a time scale, wherein the irrigation amount is equal to the total evapotranspiration amount since the last irrigation period. Chanswang et al, 2019, volume 50, journal addition of agricultural machinery, discloses a prediction model of daily transpiration of a single tomato plant, which takes variation of moisture content of a substrate, air temperature, air humidity and illumination intensity as input. Xulihong et al, in 2020, published tomato matrix cultivation irrigation model based on greenhouse environment and crop growth in the 10 th phase of volume 36 of agricultural engineering newspaper, modified the original expression of the Penman-Monteith model to remove the soil evaporation part, introduced TOMGRO model to simulate the growth of tomato canopy to correct the impedance parameter, and obtained a new transpiration model; and the influence of solar radiation on water consumption below the canopy of the crop is considered, and a daily irrigation quantity model of the single tomato is established. Li et al, 2020, Vol 51, No. 1, disclose research on strawberry irrigation strategy based on K-means clustering algorithm, and perform quantitative irrigation according to substrate water content change and daily average temperature interval. In the prior art, irrigation decision is mainly made by considering greenhouse environment parameters, substrate water content and the like, actual growth conditions of crops are not considered, prediction is only carried out by taking days as a unit, irrigation time and irrigation quantity cannot be dynamically adjusted according to the actual environment parameters and the actual water demand state of the crops on the same day, and the requirement for accurate irrigation of greenhouse crops is difficult to meet.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a greenhouse crop irrigation method based on a deep neural network, which can detect the moisture state of crops in time, dynamically adjust the irrigation time and irrigation quantity according to the moisture state of the crops and environmental parameters, and realize accurate irrigation of the crops.
The present invention achieves the above-described object by the following technical means.
A greenhouse crop irrigation method based on a deep neural network specifically comprises the following steps:
collecting greenhouse environment parameters and RGB (red, green and blue) images, depth images and near-infrared images of crop canopy leaves every m minutes, inputting the greenhouse environment parameters and the RGB images into a crop moisture state detection model, and judging the moisture state of crops;
if the water state of the crops is irrigation shortage, immediately irrigating; and carrying out next crop water state detection after the crop water stress recovery time;
if the water state of the crops is insufficient after the irrigation within n hours, judging the water state of the crops through a crop water state detection model within n hours after the irrigation, and irrigating; wherein n is the irrigation interval.
Further, the crop moisture state detection model specifically comprises: and establishing a deep neural network model for detecting the moisture state of the crops by taking the planting date, the current time, the greenhouse environment parameters, the RGB image, the depth image and the near infrared image of the leaves of the canopy of the crops as input and the moisture state of the crops as output.
Further, the irrigation deficit is a crop moisture state with an irrigation level smaller than F (1-P), and the irrigation level with the highest net photosynthetic rate is defined as an irrigation reference and is recorded as F; the allowable relative deviation is defined as a threshold value, denoted as P.
Furthermore, the crop moisture state with the irrigation level larger than F (1+ P) is judged as the excessive irrigation, and the crop moisture state in the interval [ F (1-P), F (1+ P) ] is judged as the proper irrigation.
Further, the crop water stress recovery time is predicted according to a crop water stress recovery time prediction model, wherein the crop water stress recovery time prediction model specifically comprises: and (3) establishing a deep neural network model for predicting the crop water stress recovery time by taking the planting date, the current time, the greenhouse environment parameters and the irrigation quantity of the crops as input and the crop water stress recovery time as output.
Further, the irrigation amount is determined by the following formula:
wherein: q is the irrigation quantity of the individual plant, QpredAnd (3) the predicted value of the single plant transpiration in the next n hours, Q' is the single plant irrigation amount during the last irrigation, and ET is the single plant transpiration amount from the last irrigation to the current time.
Furthermore, the predicted value Q of the transpiration amount of the single plantpredThe acquisition method comprises the following steps: inputting greenhouse environment parameters of every minute in the first 24 hours, the whole-point weather forecast information in the last n hours and the set value of the control parameter of the greenhouse environment regulation and control system into a greenhouse environment parameter prediction model to obtain greenhouse environment prediction information in the last n hours; inputting the planting date, the current time and greenhouse environment prediction information of the crops into a crop transpiration rate prediction model, and calculating to obtain the crop transpiration rate; the integral calculation of the crop transpiration rate in the last n hoursObtaining a predicted value Q of the crop transpiration amountpred。
Furthermore, the crop transpiration rate prediction model specifically comprises: and (3) taking the planting date, the current time and the greenhouse environment parameters of the crops as the input of the model, and taking the crop transpiration rate as the output of the model to construct a deep neural network model for predicting the crop transpiration rate.
Furthermore, the greenhouse environment parameter prediction model specifically comprises: and taking the greenhouse environment parameters of the first 24 hours per minute, the whole-point weather forecast information in the last n hours and the control parameter set value of the greenhouse environment regulation and control system as inputs, taking the greenhouse environment parameters of every minute in the last n hours as outputs, and constructing a deep neural network model for greenhouse environment parameter prediction.
Further, m and n satisfy (n × 60) divisible by m.
The invention has the beneficial effects that: according to the invention, the moisture state of the crops can be judged in time for irrigation according to the field planting date, the current time, the greenhouse environment parameters, the RGB image, the depth image and the near-infrared image of the crop canopy leaves, the influence of greenhouse environment regulation on the greenhouse environment is considered, and then the irrigation quantity is dynamically adjusted according to the greenhouse environment parameters and the current time, so that the accurate irrigation of the crops is realized, the irrigation water can be saved, and the waste of water resources is reduced.
Drawings
FIG. 1 is a flow chart of a greenhouse crop irrigation method based on a deep neural network.
Detailed Description
The invention will be further described with reference to the following figures and specific examples, but the scope of the invention is not limited thereto.
The embodiment takes a greenhouse tomato irrigation method based on a deep neural network as an example, and further explains the implementation process.
S1, constructing a crop transpiration rate prediction model based on the deep neural network
Cultivating the crops required by establishing the crop transpiration rate prediction model, fully irrigating the crops, recording the planting date of the crops, and collecting the current date, the current time, the greenhouse environment parameters and the crop transpiration rate once every minute.
And (3) taking the planting date, the current time and the greenhouse environment parameters of the crops as the input of the model, and taking the crop transpiration rate as the output of the model to construct a deep neural network model for predicting the crop transpiration rate. In this example, the number of the tomato transpiration rate prediction model is 3, and the number of neurons in the hidden layer is 6. And training by using the acquired data to obtain a crop transpiration rate prediction model based on the deep neural network.
In this embodiment, greenhouse environmental parameters are collected by a greenhouse weather station, and the transpiration rate of the tomatoes is measured by a wrapped stem flow system.
S2, constructing a greenhouse environment parameter prediction model based on the deep neural network
The greenhouse environment regulation and control system automatically regulates, and records greenhouse environment parameters, current weather forecast and control parameter set values of the greenhouse environment regulation and control system once per minute. The time interval between two times of irrigation of the crop is defined as the irrigation interval time, which is recorded as n and is expressed in hours, and in the example, n is 1. And taking the greenhouse environment parameters of the first 24 hours per minute, the whole-point weather forecast information in the last n hours and the control parameter set value of the greenhouse environment regulation and control system as inputs, taking the greenhouse environment parameters of every minute in the last n hours as outputs, and constructing a deep neural network model for greenhouse environment parameter prediction. In this embodiment, the number of layers of the greenhouse environment parameter prediction model is 3, and the number of neurons in the hidden layer is 45. And training by using the recorded greenhouse environment parameters, weather forecast information and control parameter set values of the greenhouse environment regulation and control system to obtain a greenhouse environment parameter prediction model based on the deep neural network.
S3, establishing a crop moisture state detection model based on a deep neural network
Cultivating the crops required for establishing the crop moisture state detection model, and setting different irrigation levels to enable the crops to be in different moisture states. Collecting the field planting date, the current time, greenhouse environment parameters, RGB images of crop canopy leaves, depth images, near-infrared images and net photosynthetic rate once per minute; defining the irrigation level with the highest net photosynthetic rate as an irrigation reference, and recording the irrigation level as F; defining the allowable relative deviation as a threshold value, which is denoted as P, and in the example, P is 5%; the crop moisture state with the irrigation level larger than F (1+ P) is judged as excessive irrigation, the crop moisture state in the interval [ F (1-P), F (1+ P) ] is judged as proper irrigation, and the crop moisture state smaller than F (1-P) is judged as insufficient irrigation.
And establishing a deep neural network model for detecting the moisture state of the crops by taking the planting date, the current time, the greenhouse environment parameters, the RGB image, the depth image and the near infrared image of the leaves of the canopy of the crops as input and the moisture state of the crops as output. And training by using the collected greenhouse environment parameters and the RGB image, the depth image, the near infrared image and the crop moisture state of the crop canopy leaf to obtain a crop moisture state detection model based on the deep neural network.
The RGB images, depth images and near-infrared images of the crop canopy leaves are collected by a D435i depth camera of RealSense.
The net photosynthetic rate is measured using a photosynthetic apparatus.
S4, establishing a crop water stress recovery time prediction model based on a deep neural network
Cultivating crops required by establishing a crop water stress recovery time prediction model, and inputting the field planting date, the current time, greenhouse environment parameters, RGB images, depth images and near-infrared images of crop canopy leaves into a crop water state detection model to obtain the crop water state. When the crops are in the irrigation shortage state, the crops are fully irrigated, and the planting date, the current time, the greenhouse environment parameters and the irrigation amount of the crops are recorded. After that, the crop water state is detected once every minute until the crop water state is no longer irrigation deficit, and the elapsed time is recorded as the crop water stress recovery time.
And (3) establishing a deep neural network model for predicting the crop water stress recovery time by taking the planting date, the current time, the greenhouse environment parameters and the irrigation quantity of the crops as input and the crop water stress recovery time as output. In this embodiment, the number of the tomato water stress time prediction model layers is 3, and the number of neurons in the hidden layer is 6. And training by using the acquired data to obtain a crop water stress recovery time prediction model based on the deep neural network.
S5, making a greenhouse crop irrigation strategy
Greenhouse environmental parameters were collected every one minute during the day. Carrying out first irrigation after sunrise time of the place of the greenhouse, and calculating irrigation quantity according to a formula (1) that the moisture state is an appropriate irrigation state;
the time interval between two crop moisture state detections is defined as the crop moisture state detection time interval, which is recorded as m, and the unit is minutes, and in this embodiment, m is 10. And (3) collecting greenhouse environment parameters and RGB (red, green and blue) images, depth images and near-infrared images of crop canopy leaves every m minutes, and inputting the information into a crop moisture state detection model to judge the moisture state of the crops. And if the water state of the crops is insufficient for irrigation, immediately irrigating, calculating the irrigation quantity by the formula (1), and detecting the water state of the crops next time after the water stress recovery time of the crops.
If the moisture state of the crops is insufficient for irrigation (namely excessive irrigation or proper irrigation) within n hours after irrigation, judging the moisture state of the crops through a crop moisture state detection model and starting an irrigation fertilizer applicator to irrigate n hours after irrigation, wherein the irrigation quantity is calculated by the formula (1):
wherein Q is the irrigation quantity of a single plant, and the unit is as follows: mL/plant;
Qpredpredicted values of the transpiration amount of each plant in the next n hours are as follows: mL/plant;
q' is the irrigation quantity of each plant during the last irrigation, and the unit is as follows: mL/plant;
ET is the transpiration of a single plant in the time period from the last irrigation to the current time, and the unit is as follows: the calculation method comprises the steps of firstly calculating the crop transpiration rate in the time period from the last irrigation to the current time according to a crop transpiration rate prediction model, and then integrating the crop transpiration rate in the time period to obtain a value, namely the crop transpiration amount in the time period.
The predicted value Q of the transpiration amount of the single plantpredIs determined by the following method: inputting the greenhouse environment parameters of the first 24 hours per minute, the whole-point weather forecast information in the last n hours and the set values of the control parameters of the greenhouse environment regulation and control system into the greenhouse environment parameter prediction model established in S2 to obtain the greenhouse environment prediction information in the last n hours; inputting the planting date, the current time and the greenhouse environment prediction information of the crops into the crop transpiration rate prediction model established in the S1 to calculate the crop transpiration rate; and performing integral calculation on the crop transpiration rate in the next n hours to obtain a crop transpiration amount predicted value Qpred。
The crop moisture status detection time interval of m minutes and the irrigation time interval of n hours are adjusted according to the crop species, and (n × 60) can be evenly divided by m.
And S6, irrigating the crops according to the greenhouse crop irrigation strategy. In this example, the fertigation machine irrigates according to a greenhouse crop irrigation strategy.
The present invention is not limited to the above-described embodiments, and any obvious improvements, substitutions or modifications can be made by those skilled in the art without departing from the spirit of the present invention.
Claims (10)
1. A greenhouse crop irrigation method based on a deep neural network is characterized in that:
collecting greenhouse environment parameters and RGB (red, green and blue) images, depth images and near-infrared images of crop canopy leaves every m minutes, inputting the greenhouse environment parameters and the RGB images into a crop moisture state detection model, and judging the moisture state of crops;
if the water state of the crops is irrigation shortage, immediately irrigating; and carrying out next crop water state detection after the crop water stress recovery time;
if the water state of the crops is insufficient after the irrigation within n hours, judging the water state of the crops through a crop water state detection model within n hours after the irrigation, and irrigating; wherein n is the irrigation interval.
2. The deep neural network-based greenhouse crop irrigation method as claimed in claim 1, wherein the crop moisture state detection model is specifically: and establishing a deep neural network model for detecting the moisture state of the crops by taking the planting date, the current time, the greenhouse environment parameters, the RGB image, the depth image and the near infrared image of the leaves of the canopy of the crops as input and the moisture state of the crops as output.
3. The deep neural network-based greenhouse crop irrigation method as claimed in claim 1, wherein the irrigation deficit is a crop moisture state with irrigation level less than F (1-P), and the irrigation level with the highest net photosynthetic rate is defined as an irrigation reference, denoted as F; the allowable relative deviation is defined as a threshold value, denoted as P.
4. The deep neural network-based greenhouse crop irrigation method as claimed in claim 3, wherein the crop moisture status with irrigation level greater than F (1+ P) is judged as irrigation excess, and the crop moisture status in the interval [ F (1-P), F (1+ P) ] is judged as irrigation proper.
5. The deep neural network-based greenhouse crop irrigation method as claimed in claim 1, wherein the crop water stress recovery time is predicted according to a crop water stress recovery time prediction model, and the crop water stress recovery time prediction model is specifically: and (3) establishing a deep neural network model for predicting the crop water stress recovery time by taking the planting date, the current time, the greenhouse environment parameters and the irrigation quantity of the crops as input and the crop water stress recovery time as output.
6. The deep neural network-based greenhouse crop irrigation method of claim 4, wherein the irrigation amount is determined by the following formula:
wherein: q is the irrigation quantity of the individual plant, QpredAnd (3) the predicted value of the single plant transpiration in the next n hours, Q' is the single plant irrigation amount during the last irrigation, and ET is the single plant transpiration amount from the last irrigation to the current time.
7. The deep neural network-based greenhouse crop irrigation method as claimed in claim 6, wherein the single plant transpiration predicted value QpredThe acquisition method comprises the following steps: inputting greenhouse environment parameters of every minute in the first 24 hours, the whole-point weather forecast information in the last n hours and the set value of the control parameter of the greenhouse environment regulation and control system into a greenhouse environment parameter prediction model to obtain greenhouse environment prediction information in the last n hours; inputting the planting date, the current time and greenhouse environment prediction information of the crops into a crop transpiration rate prediction model, and calculating to obtain the crop transpiration rate; the crop transpiration rate is integrated in the last n hours to obtain a crop transpiration amount predicted value Qpred。
8. The deep neural network-based greenhouse crop irrigation method as claimed in claim 7, wherein the crop transpiration rate prediction model is specifically: and (3) taking the planting date, the current time and the greenhouse environment parameters of the crops as the input of the model, and taking the crop transpiration rate as the output of the model to construct a deep neural network model for predicting the crop transpiration rate.
9. The deep neural network-based greenhouse crop irrigation method as claimed in claim 7, wherein the greenhouse environment parameter prediction model is specifically: and taking the greenhouse environment parameters of the first 24 hours per minute, the whole-point weather forecast information in the last n hours and the control parameter set value of the greenhouse environment regulation and control system as inputs, taking the greenhouse environment parameters of every minute in the last n hours as outputs, and constructing a deep neural network model for greenhouse environment parameter prediction.
10. The deep neural network-based greenhouse crop irrigation method as claimed in claim 1, wherein m and n satisfy (n x 60) divisible by m.
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