Solar greenhouse water and fertilizer intelligent regulation and control method based on optical radiation cumulant

Abstract

The invention discloses a solar greenhouse water and fertilizer intelligent regulation and control method based on optical radiation cumulant, and relates to the technical field of water and fertilizer regulation and control. Therefore, a more accurate water and fertilizer control strategy is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of water and fertilizer regulation, and in particular to an intelligent water and fertilizer regulation method for a solar greenhouse based on the accumulated amount of light radiation. Background Art

[0002] Water-fertilization refers to the combined use of water and fertilizer in agricultural production. It is an effective management method to improve crop yield and quality.

[0003] The water-fertilizer irrigation system (also known as the integrated water-fertilizer irrigation system) is a modern agricultural irrigation technology that effectively combines water and fertilizer. It has many important functions, including: improving the efficiency of water resource utilization, improving the efficiency of fertilizer utilization, and promoting the healthy growth of crops. The system can adjust the supply of water and fertilizer according to the growth stage and needs of crops, provide more balanced nutrition, promote crop growth, increase yield and quality, save labor and time, reduce environmental pollution, adapt to different soil and climate conditions, monitor and control in real time, and improve agricultural sustainability.

[0004] However, crops rely on photosynthesis for growth, and light conditions have an important impact on crop photosynthesis, and the cumulative amount of light radiation can reflect the light conditions of crops to a certain extent. Crop growth requires the consumption of nutrients and water. Among them, water is consumed during crop transpiration and growth. In the water regulation decision-making plan based on days, the consumption of crop growth can be ignored. Therefore, the amount of irrigation required for crops is approximately equal to the water lost by transpiration. Therefore, the amount of fertilizer and irrigation required for crop growth has a certain relationship with the cumulative amount of light radiation.

[0005] To this end, the present invention aims to provide a method for intelligent water and fertilizer control in a solar greenhouse based on the accumulated amount of light radiation to solve the above problems. Summary of the invention

[0006] The purpose of the present invention is to solve the above problems and provide a method for intelligent water and fertilizer control in a solar greenhouse based on the cumulative amount of light radiation. The present invention explores the relationship between crop irrigation and fertilization amounts and the cumulative amount of light radiation through a machine learning model, thereby achieving a more efficient water and fertilizer control strategy.

[0007] In order to achieve the above object, the technical solution of the present invention is as follows:

[0008] The present invention provides a solar greenhouse water and fertilizer intelligent control method based on the accumulated amount of light radiation, wherein the water and fertilizer automatic control method includes an irrigation control method and a fertilization control method;

[0009] The irrigation regulation method comprises the following steps:

[0010] S1. Collect environmental parameters, including accumulated light radiation, net radiation, soil heat flux, atmospheric pressure, temperature and wind speed at 2 m above the ground, and dew point temperature;

[0011] S2. Calculate the daily transpiration of crops ET using the FAO PM formula based on the collected environmental parameters O ; Choose different crop coefficients K according to different growth stages of different crops C Correct ETO and finally calculate the theoretical daily irrigation amount ET of the crop. c =ET o ·K c ;

[0012] S3. Taking the ambient temperature and humidity and the accumulated amount of light radiation as the model input, and the theoretical daily irrigation amount of the crop as the model output, a solar greenhouse irrigation control model based on the accumulated amount of light radiation is established using a machine learning method;

[0013] The fertilizer control method comprises the following steps:

[0014] S1. Measure the light radiation accumulation data in the greenhouse through a light radiation accumulation sensor;

[0015] S2. Based on the measured cumulative light radiation data and the types of crops in the greenhouse, a machine learning model is used to automatically calculate the amount of fertilizer to be applied. The calculated amount of fertilizer is used to configure the nutrient solution based on the irrigation volume of the day, and it can be used after the configuration is completed.

[0016] The FAO PM formula is:

[0017]

[0018] Among them, ET O is crop transpiration; R n is the net radiation value, measured by the net radiation sensor; G is the soil heat flux, measured by the soil heat flux sensor; γ is the hygrometer constant, γ = 0.665 × 10 -3 P; P is atmospheric pressure, measured by a barometer; T is the average temperature at a height of 2m, measured by a temperature sensor; μ 2 is the average wind speed at a height of 2m, measured by a wind speed sensor; e s is the saturated water vapor pressure, e a is the actual water vapor pressure, Tdew is the dew point temperature, measured by a dew point sensor; Δ is the slope of the saturated water vapor pressure curve.

[0019] Compared with the prior art, this solution has the following beneficial effects:

[0020] The present invention reduces waste and improves the utilization rate of water and nutrients by precisely controlling the supply of water and fertilizers; and ensures that crops obtain the required water and nutrients at different growth stages by real-time monitoring of environmental parameters and crop plant growth conditions, thereby promoting healthy growth and increasing yields. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of the water and fertilizer automatic control method in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution of the present invention will be further described in detail below in conjunction with the embodiments of the present invention and the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0023] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will be described in detail below in conjunction with the embodiments.

[0024] Example:

[0025] Modeling experiment:

[0026] The experiment selected 200 strawberry plants of the same variety, with standard fertilizer and water (the standard fertilizer and water amounts are based on the fertilizer and irrigation standards currently used by agricultural production personnel). The test time is a complete growth cycle of the strawberry (starting from greenhouse planting and ending with the natural death of the plant). During the test, sampling and testing were carried out every half a month, with 10 samples collected each time.

[0027] Objective 1: To calculate the total amount of nitrogen, phosphorus and potassium consumed by plant growth within two sampling intervals under different gradient fertilizer application rates. Calculation formula: Total nitrogen / phosphorus / potassium of plants = total nitrogen / total phosphorus / total potassium content of plants X plant mass. The total nitrogen content was detected by Kjeldahl method; the total phosphorus content was detected by molybdenum antimony colorimetry; the total potassium content was detected by flame photometry, and the plant mass was measured by analytical balance. The total amount of N, P and K consumed by plant growth within two sampling intervals calculated in the preliminary test will be converted into the amount of fertilizer, and together with the accumulated amount of light radiation detected by the sensor, the plant growth parameters (plant height, crown diameter, petiole length of the third leaf, leaf area and photosynthetic index in the fruitless period; maximum single fruit weight, fruit hardness and soluble solid content in the fruiting period) are added, and a machine learning method is used to establish a greenhouse strawberry fertilization control model based on the accumulated amount of light radiation. Plant height, crown diameter and third petiole length were measured with a ruler; leaf area was measured with a leaf area meter; photosynthetic index was measured with a photosynthetic meter; maximum single fruit weight was measured with an analytical balance; fruit hardness was tested with a hardness meter; and soluble solid content was tested with a refractometer. The theoretical fertilizer amount calculated by the model will be used for fertilization guidance in the verification experiment.

[0028] Objective 2: Collect greenhouse environmental parameters and calculate the daily transpiration ET of standard plants according to the FAO Penman-Monteith formula O According to the different growth stages of strawberries, different crop coefficients (K C ) O The final theoretical daily irrigation amount ET for strawberry plants is calculated as follows: C =ET O ·E C The accumulated light radiation and ambient temperature and humidity data obtained by the sensor are used as model inputs, and the theoretical daily irrigation amount is used as output to establish a greenhouse strawberry irrigation control model based on the accumulated light radiation. The theoretical irrigation amount calculated by the model will be used for irrigation guidance in the verification test.

[0029] Verification experiment:

[0030] The experiment was divided into four treatments, each with 200 plants. Treatment 1: standard water and standard fertilizer; Treatment 2: standard water and custom fertilizer; Treatment 3: custom water and standard fertilizer; Treatment 4: custom water and custom fertilizer (the standard fertilizer and standard water are derived from the fertilizer and irrigation standards currently used by agricultural production personnel; the custom water and custom fertilizer are derived from the fertilization / irrigation model obtained during the modeling experiment). The test time is a complete growth cycle of strawberries (starting from greenhouse planting and ending with natural death of plants). During the experiment, sampling and testing were carried out every half a month, with 10 plants collected each time to evaluate the growth of strawberries and verify the actual application effect of the model. The detection indicators of the fruitless period (mainly the vegetative growth stage of the plant) are mainly plant height, crown diameter, petiole length of the third leaf, leaf area and photosynthetic index; the detection indicators of the fruiting period (the reproductive growth stage of the plant) are mainly single fruit weight, fruit hardness and soluble solids content.

[0031] The above specific embodiments are merely explanations of the present invention and are not limitations of the present invention. After reading this specification, those skilled in the art may make modifications to the embodiments without any creative contribution as needed. However, such modifications are protected by the patent law as long as they are within the scope of the claims of the present invention.

Claims

1. A method for intelligently controlling water and fertilizer in a solar greenhouse based on the cumulative amount of light radiation, characterized by: The water and fertilizer automatic control method includes an irrigation control method and a fertilizer control method; The irrigation regulation method comprises the following steps: S1. Collect environmental parameters, including accumulated light radiation, net radiation, soil heat flux, atmospheric pressure, temperature 2 meters above the ground, humidity, wind speed, and dew point temperature; S2. Calculate the daily transpiration of crops ET using the FAO PM formula based on the collected environmental parameters O ; Choose different crop coefficients K according to different growth stages of different crops C Correct ETO and finally calculate the theoretical daily irrigation amount ET of the crop. c =ET o ·K c ; S3. Taking the ambient temperature, humidity and accumulated light radiation as the model input, taking the theoretical daily irrigation amount of the crop as the model output, and using the machine learning method to establish a solar greenhouse irrigation control model based on accumulated light radiation; The fertilizer control method comprises the following steps: S1. Measure the light radiation accumulation data in the greenhouse through a light radiation accumulation sensor; S2. Based on the measured cumulative light radiation data and the types of crops in the greenhouse, a machine learning model is used to automatically calculate the amount of fertilizer to be applied. The calculated amount of fertilizer is used to configure the nutrient solution based on the irrigation volume of the day, and it can be used after the configuration is completed.

2. The method for intelligently controlling water and fertilizer in a solar greenhouse based on the accumulated amount of light radiation according to claim 1, characterized in that: The FAO PM formula is: Among them, E.T. O is crop transpiration; R n is the net radiation value, measured by the net radiation sensor; G is the soil heat flux, measured by the soil heat flux sensor; γ is the hygrometer constant, γ = 0.665 × 10 -3 P; P is atmospheric pressure, measured by a barometer; T is the average temperature at a height of 2m, measured by a temperature sensor; μ2 is the average wind speed at a height of 2m, measured by a wind speed sensor; e s is the saturated water vapor pressure, e a is the actual water vapor pressure, Tdew is the dew point temperature, measured by a dew point sensor; Δ is the slope of the saturated water vapor pressure curve.

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