Water and fertilizer integrated drip irrigation system for cotton growth period physiological index monitoring based on Internet of Things

Abstract

The invention discloses a water and fertilizer integrated drip irrigation system for cotton growth period physiological index monitoring based on Internet of Things, and is applied to the technical field of cotton drip irrigation. Comprising an Internet of Things module, an edge computing device, a cloud platform decision module and a water and fertilizer drip irrigation device, the Internet of Things module is used for monitoring environmental data and physiological index data of cotton, a growth period recognition algorithm is built in an edge computing terminal, and the growth period of the cotton is automatically judged according to the physiological index data; the cloud platform decision-making module generates a water and fertilizer formula based on the environmental data, the physiological index data and the growth period of cotton, and the water and fertilizer drip irrigation equipment controls the drip irrigation amount in different areas and time periods based on a soil permeation model. According to the method, the problems that water and fertilizer control and physiological stages are disjointed in traditional cotton planting, and the requirement difference of different growth periods cannot be adapted are solved, corresponding drip irrigation schemes are set for different growth periods, switching is conducted through growth period recognition, and the adaptation degree of the drip irrigation schemes is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cotton drip irrigation, and more specifically, to a water and fertilizer integrated drip irrigation system based on the monitoring of physiological indexes of cotton growth periods by means of the Internet of Things. Background Technique

[0002] Scientific water and fertilizer control is very important for cotton cultivation. Rational water and fertilizer cultivation can ensure that cotton obtains appropriate amounts of water and nutrients during different growth periods, thereby promoting the normal growth and development of cotton, significantly improving the yield and quality of cotton. In addition, it can also save water resources, reduce the occurrence of pests and diseases, save costs, and improve economic benefits. The growth period of cotton can be divided into the seedling stage, budding stage, flowering and boll stage, and boll opening stage. Different growth periods have different characteristics and different water and fertilizer requirements. Therefore, it is necessary to set different water and fertilizer control schemes for different growth periods. However, in the existing cotton cultivation technology, water and fertilizer management depends on artificial experience, which is prone to cause waste of resources or imbalance between supply and demand. Moreover, the existing drip irrigation systems lack dynamic perception of the physiological state of cotton, making it difficult to adjust water and fertilizer strategies according to the requirements of the growth period. The correlation between environmental parameters and physiological indexes has not been fully explored, resulting in low accuracy of irrigation decision-making. Therefore, how to provide a water and fertilizer integrated drip irrigation system based on the monitoring of physiological indexes of cotton growth periods by means of the Internet of Things is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0003] In view of this, the present invention provides a water and fertilizer integrated drip irrigation system based on the monitoring of physiological indexes of cotton growth periods by means of the Internet of Things. The environmental data and the physiological index data of cotton are monitored by the Internet of Things module. The edge computing terminal judges the growth period of cotton, the cloud platform decision-making module generates a water and fertilizer formula, and finally the water and fertilizer drip irrigation equipment realizes drip irrigation.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] A water and fertilizer integrated drip irrigation system based on the monitoring of physiological indexes of cotton growth periods by means of the Internet of Things, comprising an Internet of Things module, an edge computing device, a cloud platform decision-making module, and a water and fertilizer drip irrigation device. The Internet of Things module is used for monitoring environmental data and physiological index data of cotton. The edge computing terminal is built-in with a growth period recognition algorithm and automatically judges the growth period of cotton according to the physiological index data. The cloud platform decision-making module generates a water and fertilizer formula based on the environmental data, the physiological index data, and the growth period of cotton. The water and fertilizer drip irrigation device controls the drip irrigation amount in different regions and at different times based on the soil infiltration model.

[0006] Optionally, the Internet of Things module includes a physiological index sensing device, an environmental sensing device, and a low-power wireless networking device. The physiological index sensing device is used for monitoring physiological index data, the environmental sensing device is used for monitoring environmental data, and the low-power wireless networking device realizes the aggregation and upload of field sensor data.

[0007] Optionally, the growth stage recognition algorithm classifies and predicts the cotton growth stage based on a pre-trained LSTM network. The input data of the LSTM network is the physiological index data of cotton, and the output is the predicted probability distribution of the cotton growth stage.

[0008] Optionally, the cloud platform decision-making module generates a water and fertilizer formula based on environmental data, physiological index data, and the growth stage of the cotton, specifically as follows:

[0009] Set different application rates of nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer respectively to generate multiple groups of water and fertilizer formulas. Set up experimental cotton planting areas for all water and fertilizer formulas. Obtain the physiological index data of cotton at different growth stages of cotton, analyze the effects of nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer on the physiological index data of cotton, and set the optimal application rate ratios of nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer;

[0010] Calculate the water demand of cotton at different growth stages based on environmental data, physiological index data, and the growth stage of the cotton;

[0011] Determine the water and fertilizer formula based on the optimal application rate ratios and water demand of cotton at different growth stages.

[0012] Optionally, setting the optimal application rate ratios of nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer specifically is as follows:

[0013] Fix the ratios between other components in the water and fertilizer formula. Set different application rates of nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer respectively to generate multiple groups of water and fertilizer formulas. Set up experimental cotton planting areas for all water and fertilizer formulas. Obtain the physiological index data of cotton at different growth stages of cotton, analyze the effects of nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer on the physiological index data of cotton, determine the effects of nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer on the growth and development of cotton at different growth stages, obtain the final yield of the experimental cotton planting areas, respectively obtain the corresponding relationships between the application rates of nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer and the final yield of cotton at different growth stages, fit the relationship between the yield and the application rate of fertilizer, use the application rate of fertilizer corresponding to the highest yield as the total application rate of fertilizer for the entire growth period of cotton, and calculate the application rates of nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer for each fertilization of cotton in combination with the total application rate of fertilizer to obtain the optimal application rate ratios of nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer.

[0014] Optionally, calculating the water demand of cotton at different growth stages specifically is as follows:

[0015] ET C = K C ET0

[0016]

[0017] In the formula, ET C is the water demand, K C is the crop coefficient of cotton, and ET0 is the reference crop water demand, Rn is the net surface radiation, G is the soil heat flux, e s is the saturation vapor pressure, e d is the actual vapor pressure, U is the wind speed, γ is the humidity counting constant, and Δ is the slope of the saturation vapor pressure - temperature curve.

[0018] Optionally, when calculating the water requirement of cotton at different growth stages, the crop coefficient of cotton is corrected. The growth stages of cotton are divided into the seedling stage, budding stage, flowering and boll stage, and boll opening stage. Initial values are set for each growth stage. At the seedling stage, the crop coefficient is corrected based on soil evaporation:

[0019]

[0020] In the formula, K c1 is the correction value of the crop coefficient at the seedling stage, is the initial value of the crop coefficient at the seedling stage, f c is the crop coverage, K cmax is the maximum evaporation coefficient of the wet soil;

[0021] At all growth stages, the crop coefficient is corrected based on water stress:

[0022]

[0023] In the formula, K c is the correction value of the crop coefficient, is the initial value of the crop coefficient;

[0024] At all growth stages, the crop coefficient is corrected based on the physiological index data of cotton: when the leaf moisture content is lower than 80%, the crop coefficient increases by 5% - 10%, and when the photosynthetic rate drops by 20%, the crop coefficient decreases by 5%.

[0025] Optionally, the drip irrigation amount is controlled by region and time period based on the soil infiltration model, specifically:

[0026] Obtain the drip emitter flow rate data, soil environment data, and the growth stage of cotton, and calculate the drip irrigation duration based on the soil infiltration model:

[0027] I t =(θ s -θ i )· Z f

[0028]

[0029] In the formula, I t is the cumulative infiltration amount, θ s is the saturation water content, θ i is the actual water content, Z fis the target wetting front depth, t is the drip irrigation duration, and K s is the soil hydraulic conductivity, and S f is the wetting front suction; determine the water drip irrigation plan based on the water demand of cotton, and correct it based on the soil environment data of different regions;

[0030] Obtain the water and fertilizer formula during the growth period of cotton, calculate the fertilizer injection time to obtain the fertilization plan, and the water and fertilizer drip irrigation equipment performs drip irrigation based on the water drip irrigation plan and the fertilization plan.

[0031] As can be seen from the above technical solutions, compared with the prior art, the present invention provides a water and fertilizer integrated drip irrigation system based on the monitoring of physiological indicators during the cotton growth period by the Internet of Things, and has the following beneficial effects: The present invention monitors the environmental data and the physiological indicator data of cotton through the Internet of Things module, judges the growth period of cotton through the edge computing terminal, sets different water and fertilizer formulas for different growth periods, solves the problem that the water and fertilizer control is out of touch with the growth period in traditional cotton planting and cannot adapt to the demand differences of different growth periods, can save water and fertilizer, and at the same time increase the cotton yield. The water and fertilizer drip irrigation equipment realizes millimeter-level precise irrigation through the dynamic combination of the soil infiltration model and the Internet of Things data, and significantly improves the water and fertilizer utilization efficiency. Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0033] Figure 1 is the schematic diagram of the water and fertilizer integrated drip irrigation system of the present invention. Detailed Embodiments

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0035] The embodiments of the present invention disclose a water and fertilizer integrated drip irrigation system based on the monitoring of physiological indicators during the cotton growth period by the Internet of Things, as Figure 1As shown in the figure, it includes an Internet of Things module, an edge computing device, a cloud platform decision-making module, and a water and fertilizer drip irrigation device. The Internet of Things module is used to monitor environmental data and physiological index data of cotton. The edge computing terminal is built with a growth period recognition algorithm, which automatically determines the growth period of cotton according to the physiological index data. The cloud platform decision-making module generates a water and fertilizer formula based on the environmental data, physiological index data, and the growth period of cotton. The water and fertilizer drip irrigation device controls the drip irrigation amount in different regions and at different times based on the soil infiltration model.

[0036] Furthermore, the Internet of Things module includes a physiological index sensing device, an environmental sensing device, and a low-power wireless networking device. The physiological index sensing device is used to monitor physiological index data, the environmental sensing device is used to monitor environmental data, and the low-power wireless networking device realizes the aggregation and upload of field sensor data.

[0037] In the embodiment of the present invention, the physiological index data includes leaf temperature, stem change, leaf moisture content, and photosynthetic rate, and the environmental data includes soil type, soil wetting front suction, water vapor pressure, wind speed, surface net radiation, soil temperature and humidity, light intensity, air temperature and humidity, soil heat flux, and CO2 concentration.

[0038] In the embodiment of the present invention, the low-power wireless networking device uses a LoRa / ZigBee module to realize the aggregation of field sensor data.

[0039] Furthermore, the growth period recognition algorithm classifies and predicts the growth period of cotton based on a pre-trained LSTM network. The input data of the LSTM network is the physiological index data of cotton, and the output is the predicted probability distribution of the growth period of cotton.

[0040] In the embodiment of the present invention, the training process of the LSTM network is as follows: Obtain the physiological index data of cotton in different growth periods, manually label the data, and then preprocess the data through normalization and sliding window. Use the physiological index data of cotton as the input data and the corresponding label as the output data, and use the Adam optimization algorithm for training.

[0041] The LSTM network includes an input layer, an LSTM layer, and an output layer. The LSTM layer is composed of a forget gate, an input gate, and an output gate. The forget gate determines the retention degree of each state through the retention parameter f t The retention parameter f t passes through the Sigmoid activation function, and its value range is 0-1;

[0042] f t =σ(W f ·[h t-1 , x t +b f )

[0043] In the formula, Wf is the weight matrix of the forget gate, b f is the bias vector of the forget gate, and σ(·) represents the Sigmoid activation function; h t-1 is the hidden state at the previous moment, and x t is the input at the current moment;

[0044] The input gate updates the cell state to obtain the updated value i t and the candidate value

[0045] i t = σ(W i · [h t-1 , x t + b i )

[0046]

[0047] In the formula, W i , W C are the weight matrices of the input gate and the candidate value respectively, and b i , b C are the bias vectors of the input gate and the candidate value respectively;

[0048] The output gate generates the output value o through the Sigmoid function t ;

[0049] o t = σ(W o · [h t-1 , x t + b o )

[0050] In the formula, W o is the weight matrix of the output gate, and b o is the bias vector of the output gate;

[0051] Based on the state value c at the previous period t-1 , the retention parameter f t , the candidate value and the updated value i t calculate the state value c at the current period t :

[0052]

[0053] Based on the state value c at the current moment t and the output value o t calculate the hidden state h at the current moment t :

[0054] h t = ot *tanh(C t )。

[0055] After passing through the output layer, the probability distributions of the four growth periods are obtained to determine the current growth period.

[0056] Furthermore, the cloud platform decision module generates specific water and fertilizer formulas based on environmental data, physiological index data, and the growth period of cotton, specifically:

[0057] Set different application rates of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer respectively to generate multiple groups of water and fertilizer formulas. Set up experimental cotton planting areas for all water and fertilizer formulas. Obtain the physiological index data of cotton at different growth periods of cotton, analyze the effects of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer on the physiological index data of cotton, and set the optimal application rate ratios of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer;

[0058] Calculate the water requirement of cotton at different growth periods based on environmental data, physiological index data, and the growth period of cotton;

[0059] Determine the water and fertilizer formula based on the optimal application rate ratios and water requirements of cotton at different growth periods.

[0060] Furthermore, the specific optimal application rate ratios of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer are set as follows:

[0061] Fix the ratios between other components in the water and fertilizer formula, set different application rates of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer respectively to generate multiple groups of water and fertilizer formulas. Set up experimental cotton planting areas for all water and fertilizer formulas. Obtain the physiological index data of cotton at different growth periods of cotton, analyze the effects of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer on the physiological index data of cotton, determine the effects of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer on the growth and development of cotton at different growth periods, obtain the final yield of the experimental cotton planting areas, respectively obtain the corresponding relationships between the application rates of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer and the final yield of cotton at different growth periods, fit the relationship between the yield and the application rate, take the application rate corresponding to the highest yield as the total application rate of fertilizers for the whole growth period of cotton, and calculate the application rates of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer for each fertilization of cotton in combination with the total application rate to obtain the optimal application rate ratios of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer.

[0062] In the embodiments of the present invention, the relationship equations between the yield and the application rate are respectively fitted for each growth period of cotton. Based on the relationship equations of different growth periods, the amount of fertilizer to be applied in different growth periods can be calculated, and then the amount of fertilizer applied each time can be determined based on data such as the length of each growth period and the number of fertilizations.

[0063] Furthermore, the specific calculation of the water requirement of cotton at different growth periods is as follows:

[0064] ET C =K C ET0

[0065]

[0066] In the formula, ET C is the water requirement, K C is the crop coefficient of cotton, ET0 is the reference crop water requirement, R n is the net surface radiation, G is the soil heat flux, e s is the saturation vapor pressure, e d is the actual vapor pressure, U is the wind speed, γ is the humidity counting constant, and Δ is the slope of the saturation vapor pressure - temperature curve.

[0067] Furthermore, when calculating the water requirement of cotton at different growth stages, the crop coefficient of cotton is corrected. The growth stages of cotton are divided into the seedling stage, budding stage, flowering and boll stage, and boll opening stage. An initial value is set for each growth stage. At the seedling stage, the crop coefficient is corrected based on soil evaporation:

[0068]

[0069] In the formula, K c1 is the corrected value of the crop coefficient at the seedling stage, is the initial value of the crop coefficient at the seedling stage, f c is the crop coverage, K cmax is the maximum evaporation coefficient of the wet soil;

[0070] At all growth stages, the crop coefficient is corrected based on water stress:

[0071]

[0072] In the formula, K c is the corrected value of the crop coefficient, is the initial value of the crop coefficient;

[0073] At all growth stages, the crop coefficient is corrected based on the physiological index data of cotton: when the leaf moisture content is lower than 80%, the crop coefficient increases by 5% - 10%, and when the photosynthetic rate drops by 20%, the crop coefficient decreases by 5%.

[0074] In the embodiment of the present invention, the initial crop coefficients of cotton at the seedling stage, budding stage, flowering and boll stage, and boll opening stage are 0.34, 0.71, 1.07, and 0.78 respectively. Based on different specific planting environments, fine-tuning can also be performed according to climate, soil conditions, and planting density.

[0075] Furthermore, controlling the drip irrigation amount by region and time period based on the soil infiltration model is specifically as follows:

[0076] Obtain the drip emitter flow rate data, soil environment data, and the growth stage of cotton, and calculate the drip irrigation duration based on the soil infiltration model:

[0077] I t = (θ s - θ i )·Z f

[0078]

[0079] wherein, I t is the cumulative infiltration amount, θ s is the saturated water content, θ i is the actual water content, Z f is the target wetting front depth, t is the drip irrigation duration, K s is the soil hydraulic conductivity, S f is the wetting front suction; the drip irrigation scheme of water droplets is determined based on the water demand of cotton and corrected based on the soil environment data of different regions;

[0080] In the embodiment of the present invention, the root depth of cotton is used as the target wetting front depth; the drip irrigation duration t is calculated as the time for a single drip irrigation to make the cumulative infiltration amount reach the target wetting front depth. Based on the water demand of cotton, the total drip irrigation amount for each drip irrigation can be obtained. According to the drip head flow rate during drip irrigation, the preset drip irrigation times and the drip irrigation interval, for example, each drip irrigation is completed in two times with an interval of 6 hours each time, and the drip irrigation scheme of water droplets can be obtained;

[0081] Obtain the water and fertilizer formula during the growth period of cotton, calculate the fertilizer injection time to obtain the fertilization scheme, and the water and fertilizer drip irrigation equipment performs drip irrigation based on the drip irrigation scheme of water droplets and the fertilization scheme.

[0082] In the embodiment of the present invention, the fertilizer injection time is specifically:

[0083]

[0084] wherein, t z is, N is the fertilization amount, D is the fertilizer solution concentration, L is the drip head flow rate; the fertilizer is injected simultaneously with the drip irrigation of water and starts 10 minutes after the start of irrigation. During the fertilization process, the soil pH value is monitored to adjust the fertilization.

[0085] In the embodiment of the present invention, monitoring is carried out after drip irrigation to feedback whether the water and fertilizer formula meets the requirements. For example, it is judged whether the drip irrigation amount is up to standard according to the water content of cotton roots and the water content of leaves. If it is not up to standard, the drip irrigation duration can be appropriately increased for the next drip irrigation. If the infiltration is excessive, the drip irrigation duration can be considered to be reduced.

[0086] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same and similar parts among the embodiments can be referred to each other.

[0087] The foregoing description of the disclosed embodiments enables those skilled in the art to practice or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A water and fertilizer integrated drip irrigation system based on the monitoring of physiological indexes of cotton growth period in the Internet of Things, characterized in that, It includes an Internet of Things module, an edge computing device, a cloud platform decision-making module, and a water and fertilizer drip irrigation device. The Internet of Things module is used to monitor environmental data and physiological index data of cotton. The edge computing terminal is built with a growth period recognition algorithm, which automatically determines the growth period of cotton according to the physiological index data. The cloud platform decision-making module generates a water and fertilizer formula based on the environmental data, physiological index data, and the growth period of cotton. The water and fertilizer drip irrigation device controls the drip irrigation amount in different areas and at different times based on the soil infiltration model.

2. The water and fertilizer integrated drip irrigation system based on the monitoring of physiological indexes during the cotton growth period of the Internet of Things according to claim 1, characterized in that, The Internet of Things module includes a physiological index sensing device, an environmental sensing device, and a low-power wireless networking device. The physiological index sensing device is used to monitor physiological index data, the environmental sensing device is used to monitor environmental data, and the low-power wireless networking device realizes the aggregation and upload of field sensor data.

3. The water and fertilizer integrated drip irrigation system based on the monitoring of physiological indexes during the cotton growth period of the Internet of Things according to claim 1, characterized in that, The growth period recognition algorithm classifies and predicts the growth period of cotton based on a pre-trained LSTM network. The input data of the LSTM network is the physiological index data of cotton, and the output is the predicted probability distribution of the growth period of cotton.

4. An integrated water and fertilizer drip irrigation system based on monitoring physiological indicators of cotton growth stages in the Internet of Things according to claim 1, characterized in that, The cloud platform decision-making module generates a water and fertilizer formula based on the environmental data, physiological index data, and the growth period of cotton specifically as follows: Set different application rates of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer respectively to generate multiple groups of water and fertilizer formulas. Set experimental cotton planting areas for all water and fertilizer formulas. Obtain the physiological index data of cotton at different growth periods of cotton, analyze the effects of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer on the physiological index data of cotton, and set the optimal application rate ratios of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer. Calculate the water demand of cotton at different growth periods based on the environmental data, physiological index data, and the growth period of cotton. Determine the water and fertilizer formula based on the optimal application rate ratios and water demand of cotton at different growth periods.

5. The water and fertilizer integrated drip irrigation system based on the monitoring of physiological indexes of cotton growth period in the Internet of Things according to claim 4, characterized in that, Setting the optimal application rate ratios of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer specifically as follows: Fix the ratios between other components in the water and fertilizer formula, set different application rates of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer respectively to generate multiple groups of water and fertilizer formulas. Set experimental cotton planting areas for all water and fertilizer formulas. Obtain the physiological index data of cotton at different growth periods of cotton, analyze the effects of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer on the physiological index data of cotton, determine the effects of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer on the growth and development of cotton at different growth periods, obtain the final yield of the experimental cotton planting areas, respectively obtain the corresponding relationships between the application rates of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer and the final yield of cotton at different growth periods, fit the relationship between the yield and the application rate, take the application rate corresponding to the highest yield as the total application rate of fertilizers for the whole growth period of cotton, and calculate the application rates of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer for each fertilization of cotton in combination with the total application rate to obtain the optimal application rate ratios of nitrogen fertilizer, phosphate fertilizer, and potassium fertilizer.

6. The water and fertilizer integrated drip irrigation system based on the monitoring of physiological indexes of cotton growth period in the Internet of Things according to claim 4, characterized in that, Calculating the water demand of cotton at different growth periods specifically as follows: ET C = K C ET0 where ET C is the water requirement, K C is the crop coefficient of cotton, ET0 is the reference crop water requirement, R n is the net surface radiation, G is the soil heat flux, e s is the saturation vapor pressure, e d is the actual vapor pressure, U is the wind speed, γ is the psychrometric constant, and Δ is the slope of the saturation vapor pressure - temperature curve.

7. The integrated water and fertilizer drip irrigation system based on the monitoring of physiological indexes during the cotton growth period of the Internet of Things according to claim 6, wherein, When calculating the water demand of cotton at different growth periods, correct the crop coefficient of cotton. Divide the growth period of cotton into the seedling stage, budding stage, flowering and boll stage, and boll opening stage, and set an initial value for each growth period. Correct the crop coefficient based on soil evaporation at the seedling stage: Where, K c1 is the correction value of the crop coefficient at the seedling stage, is the initial value of the crop coefficient at the seedling stage, f c is the crop coverage, K cmax is the maximum evaporation coefficient of the wet soil; At all growth periods, correct the crop coefficient based on water stress: where K c is the correction value of the crop coefficient, and is the initial value of the crop coefficient; During all growth stages, the crop coefficient is corrected based on the physiological index data of cotton: when the leaf moisture content is lower than 80%, the crop coefficient increases by 5% - 10%, and when the photosynthetic rate drops by 20%, the crop coefficient decreases by 5%.

8. The integrated water and fertilizer drip irrigation system based on the monitoring of physiological indexes of cotton growth period in the Internet of Things according to claim 1, characterized in that, Controlling the drip irrigation amount by region and time period based on the soil infiltration model is specifically as follows: Obtain the drip emitter flow rate data, soil environment data, and the growth stage of cotton, and calculate the drip irrigation duration based on the soil infiltration model: I t = (θ s - θ i ) · Z f Where, I t is the cumulative infiltration amount, θ s is the saturated water content, θ i is the actual water content, Z f is the target wetting front depth, t is the drip irrigation duration, K s is the soil hydraulic conductivity, S f is the wetting front suction; the drip irrigation scheme for water is determined based on the water requirement of cotton and corrected based on the soil environment data of different regions; Obtain the water and fertilizer formula for the growth stage of cotton, calculate the fertilizer injection time to obtain the fertilization plan, and the water and fertilizer drip irrigation equipment performs drip irrigation based on the water drip irrigation plan and the fertilization plan.

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