Intelligent decision-making method and device for agricultural balanced fertilization considering environmental impact

Abstract

The present application relates to an intelligent decision-making method and device for balanced agricultural fertilization that takes environmental impact into consideration. The final calculated input-output ratio D is the ratio of the net additional income from fertilization to the cost of fertilization. This application compares the profitability under fertilization conditions and under no fertilization conditions, taking into account the economic value of residual fertilizers in the region and the environmental impact value caused by fertilizer losses. The smaller the input-output ratio, the higher the economic degree. By calculating the input-output ratios of different regions / plots, it is convenient to select the optimal fertilization plan.

Description

Technical Field

[0001] The present application belongs to the field of green agricultural technology, and in particular, relates to an intelligent decision-making method and device for agricultural balanced fertilization taking environmental impact into consideration. Background Art

[0002] Variable fertilization technology is one of the core contents of precision agriculture. It uses information technology and implements positioning, timing, and quantitative fertilization prescription farming based on the spatial and temporal variations of crops and their growth environment. Its purpose is to reduce fertilizer input to achieve the same benefits, improve the ecological environment of farmland, increase fertilizer utilization, and achieve the best environmental and economic benefits.

[0003] The key to variable fertilization technology is to evaluate the economic efficiency of fertilizer application, that is, how much fertilizer is the most economical and can bring about a balance between output and fertilizer application. At present, there is still a lack of evaluation methods that consider the economic efficiency of agricultural fertilizer application from multiple aspects. Summary of the invention

[0004] The technical problem to be solved by the present invention is: to solve the deficiencies in the prior art, thereby providing an intelligent decision-making method and device for agricultural balanced fertilization taking environmental impact into consideration.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] An intelligent decision-making method for balanced agricultural fertilization considering environmental impacts.

[0007] The following steps are involved:

[0008] S1: Obtain the initial soil fertilizer content Z0 of the area where crops are to be planted before cultivation, obtain the current fertilizer price P, obtain the crop yield Yk without fertilizer and the crop yield Yp with fertilizer, obtain the residual fertilizer content Z in the soil after crop harvest, obtain the unit crop fertilizer absorption content Sk without fertilizer and the unit crop fertilizer absorption content Sp with fertilizer, and take the amount of fertilizer H used during cultivation as the independent variable, H≥0;

[0009] S2: Calculate the total amount of fertilizer absorbed by the crop Xp, which is the total amount of fertilizer absorbed by the crop Xp = crop yield Yp × unit crop fertilizer absorption content Sp;

[0010] Calculate the total amount of crop absorption without fertilizer Xk, the total amount of crop absorption without fertilizer Xk = crop yield Yk × unit crop fertilizer absorption content Sk without fertilizer;

[0011] Calculate the amount of fertilizer loss, fertilizer loss S = × total amount of fertilizer absorbed by crops Xp / area; fertilizer utilization rate I% = / amount of fertilizer used during farming H × 100%;

[0012] S3: Calculate the production ratio D curve as a function of the fertilizer amount H. Among them, P represents the price of fertilizer, and Py represents the price of crop products;

[0013] S4: Find the minimum value of the input-output ratio D curve, and obtain the value of the fertilizer amount H at this time as the optimal fertilizer amount.

[0014] Preferably, in the intelligent decision-making method for balanced agricultural fertilization considering environmental impact of the present invention, the fertilizers are nitrogen fertilizer, phosphorus fertilizer and potassium fertilizer;

[0015] During the calculation process, the fertilizer amount H of the three fertilizers, the residual fertilizer content Z in the soil after crop harvest, the fertilizer absorption content Sk of the unit crop without fertilizer application, and the fertilizer absorption content Sp of the unit crop with fertilizer application are obtained respectively; the fertilizer loss of the three fertilizers is calculated respectively;

[0016] When calculating the input-output ratio D, the three fertilizers need to be added to the formula described in S3.

[0017] Preferably, in the intelligent decision-making method for balanced agricultural fertilization considering environmental impact of the present invention, the method for obtaining the unit crop fertilizer absorption content Sp of fertilization and the unit crop fertilizer absorption content Sk of no fertilization is:

[0018] The harvested crops are air-dried, and some of the crops are randomly selected as samples using statistical methods to measure the content of fertilizer elements in the crops. Then, the unit crop fertilizer absorption content Sp for fertilization and the unit crop fertilizer absorption content Sk for no fertilization are estimated based on the sample proportions.

[0019] Preferably, in the intelligent decision-making method for balanced agricultural fertilization taking environmental impact into consideration of the present invention, the air-dried plants include crop seeds, stems, leaves, and roots, and the air-dried plants are crushed to determine the fertilizer element content.

[0020] Preferably, the intelligent decision-making method for balanced agricultural fertilization considering environmental impact of the present invention uses a convolutional neural network model to measure the fertilizer element content of the air-dried stems and leaves and the fertilizer element content of the grains;

[0021] The method comprises the following steps:

[0022] A1: Convolutional neural network model device training process: The convolutional neural network model device is trained with the photos of stems, leaves and seeds air-dried to a specific moisture content and the corresponding crop training set of the fertilizer element content of the stems and leaves and the fertilizer element content of the seeds to obtain the convolutional neural network model device; when constructing the training set, according to the normal distribution, several intervals are divided according to the quality of the stems, leaves or seeds, and the corresponding number of samples are selected to construct the training set. The stems, leaves or seeds of the training set are complete photos, and the non-stem, leaves or seeds in the photos are adjusted to be transparent through processing;

[0023] A2: Element content recognition process, taking photos of new unknown stem and leaf fertilizer element content and seed fertilizer element content air-dried to a specific moisture content as input, and taking the output of the convolutional neural network model as the stem and leaf fertilizer element content and seed fertilizer element content;

[0024] During the identification process, stems, leaves or seeds whose mass conforms to the normal distribution are also selected as samples. The fertilizer element content in each identified stem, leaf or seed is summed up and then divided by the corresponding weight percentage to obtain the fertilizer element content of the test plot.

[0025] 6. An intelligent decision-making device for balanced agricultural fertilization taking environmental impact into consideration, comprising:

[0026] Data acquisition module: used to obtain the initial fertilizer content Z0 of the soil in the area where crops are to be planted before cultivation, obtain the current fertilizer price P, obtain the amount of fertilizer H used during cultivation, obtain the crop yield Yk without fertilizer and the crop yield Yp with fertilizer, obtain the residual fertilizer content Z in the soil after crop harvest, obtain the unit crop fertilizer absorption content Sk without fertilizer and the unit crop fertilizer absorption content Sp with fertilizer;

[0027] Data processing module: used to calculate the total amount of crop absorption Xp after fertilization, which is the total amount of crop absorption Xp = crop yield Yp × fertilizer absorption content Sp per unit of crop after fertilization; calculate the total amount of crop absorption Xk without fertilization, which is the total amount of crop absorption Xk without fertilization = crop yield Yk × fertilizer absorption content Sk per unit of crop without fertilization; calculate the amount of fertilizer loss, which is the amount of fertilizer loss S = × total amount of crop absorption Xp after fertilization / area; fertilizer utilization rate I% = / amount of fertilizer used during cultivation H × 100%;

[0028] Economic evaluation module: used to calculate the investment-production ratio D,

[0029] Among them, P represents the price of fertilizer, and Py represents the price of crop products;

[0030] Optimal evaluation module: used to solve the minimum value of the input-output ratio D curve and obtain the value of the fertilizer amount H at this time as the optimal fertilizer amount.

[0031] Preferably, in the intelligent decision-making device for balanced agricultural fertilization considering environmental impact of the present invention, the fertilizers are nitrogen fertilizer, phosphorus fertilizer and potassium fertilizer;

[0032] The calculation process of the data processing module and the economic evaluation module respectively obtains the fertilizer amount H of the three fertilizers, the residual fertilizer content Z in the soil after crop harvesting, the unit crop fertilizer absorption content Sk without fertilizer application, and the unit crop fertilizer absorption content Sp with fertilizer application; and calculates the fertilizer loss of the three fertilizers respectively;

[0033] When calculating the input-output ratio D, the three fertilizers need to be added to the formula described in S3.

[0034] Preferably, in the intelligent decision-making device for balanced agricultural fertilization considering environmental impact of the present invention, the method for obtaining the unit crop fertilizer absorption content Sp of fertilization and the unit crop fertilizer absorption content Sk of no fertilization is:

[0035] The harvested crops are air-dried, and some of the crops are randomly selected as samples using statistical methods to measure the content of fertilizer elements in the crops. Then, the unit crop fertilizer absorption content Sp for fertilization and the unit crop fertilizer absorption content Sk for no fertilization are estimated based on the sample proportions.

[0036] Preferably, in the intelligent decision-making device for balanced agricultural fertilization taking environmental impact into consideration of the present invention, the air-dried plants include crop seeds, stems, leaves, and roots, and the air-dried plants are crushed to determine the fertilizer element content.

[0037] The present invention also provides a computer storage medium storing one or more instructions, wherein the instructions are suitable for being loaded by a processor and executing the above-mentioned intelligent decision-making method for balanced agricultural fertilization taking environmental impact into consideration.

[0038] The beneficial effects of the present invention are:

[0039] The intelligent decision-making method and device for balanced agricultural fertilization considering environmental impact of the present application, the final calculated investment-production ratio D, is the ratio of the newly added net income of fertilization to the cost of fertilization. In this embodiment, the profitability under fertilization and without fertilization is compared, taking into account the economic value of residual fertilizers in the region and the environmental impact value caused by fertilizer loss. The smaller the investment-production ratio, the higher the economic degree. By calculating the investment-production ratios of different regions (plots), the optimal fertilization plan can be easily selected. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The technical solution of the present application is further described below in conjunction with the accompanying drawings and embodiments.

[0041] Figure 1 is a flow chart of an intelligent decision-making method for balanced agricultural fertilization considering environmental impact according to an embodiment of the present application;

[0042] Figure 2 It is a structural diagram of the convolutional neural network model device of an embodiment of the present application. DETAILED DESCRIPTION

[0043] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other.

[0044] The technical solution of the present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0045] Example 1

[0046] This embodiment provides an intelligent decision-making method for balanced fertilization in agriculture taking environmental impact into consideration. Figure 1 As shown, the following steps are included:

[0047] S1: Obtain the initial fertilizer content Z0 of the soil in the area where crops are to be planted before cultivation, obtain the current fertilizer price P, obtain the amount of fertilizer H used during cultivation, obtain the crop yield Yk without fertilizer and the crop yield Yp with fertilizer, obtain the residual fertilizer content Z in the soil after crop harvest, obtain the unit crop fertilizer absorption content Sk without fertilizer and the unit crop fertilizer absorption content Sp with fertilizer;

[0048] S2: Calculate the total amount of fertilizer absorbed by the crop Xp, which is the total amount of fertilizer absorbed by the crop Xp = crop yield Yp × unit crop fertilizer absorption content Sp;

[0049] Calculate the total amount of crop absorption without fertilizer Xk, the total amount of crop absorption without fertilizer Xk = crop yield Yk × unit crop fertilizer absorption content Sk without fertilizer;

[0050] Calculate the amount of fertilizer loss, fertilizer loss S = (1-I%) × total amount of fertilizer absorbed by crops Xp / area; fertilizer utilization rate I% = (total amount of fertilizer absorbed by crops Xp-total amount of fertilizer absorbed by crops without fertilizer Xk-residual fertilizer content in soil after harvest Zi) / amount of fertilizer used during cultivation H×100%;

[0051] S3: Calculate the production ratio D, Among them, P represents the price of fertilizer and Py represents the price of crop products.

[0052] The intelligent decision-making method for balanced agricultural fertilization considering environmental impact in this embodiment finally calculates the input-output ratio D, which is the ratio of the net income added by fertilization to the cost of fertilization. In this embodiment, the profitability under fertilization and under no fertilization is compared, taking into account the economic value of the residual fertilizer in the region and the environmental impact value caused by the loss of fertilizer. The smaller the input-output ratio, the higher the economic degree. By calculating the input-output ratio of different regions (plots), it is convenient to select the optimal fertilization plan.

[0053] Preferably, in the intelligent decision-making method for balanced agricultural fertilization considering environmental impact of the present invention, the fertilizers are nitrogen fertilizer, phosphorus fertilizer and potassium fertilizer;

[0054] During the calculation process, the fertilizer amount H of the three fertilizers, the residual fertilizer content Z in the soil after crop harvest, the fertilizer absorption content Sk of the unit crop without fertilizer application, and the fertilizer absorption content Sp of the unit crop with fertilizer application are obtained respectively; the fertilizer loss of the three fertilizers is calculated respectively;

[0055] When calculating the input-output ratio D, the three fertilizers need to be added to the formula described in S3.

[0056] i=1 may represent nitrogen fertilizer, 2 may represent phosphorus fertilizer, and 3 may represent potassium fertilizer. Of course, evaluation of other fertilizer elements may also be performed.

[0057] In step S2: the method for obtaining the unit crop fertilizer absorption content Sp of fertilization and the unit crop fertilizer absorption content Sk of no fertilization is:

[0058] The harvested crops are air-dried, and some of the crops are randomly selected as samples using statistical methods to measure the content of fertilizer elements in the crops. Then, the unit crop fertilizer absorption content Sp for fertilization and the unit crop fertilizer absorption content Sk for no fertilization are estimated based on the sample proportions.

[0059] The air-dried plants include crop seeds, stems, leaves and roots. The air-dried plants are crushed and then the fertilizer element content is determined.

[0060] Taking rice as an example, the yield and fertilizer element content determination method is as follows:

[0061] Sampling: Randomly take 3 sample sections of 1m in length: After the whole crop is air-dried, weigh the air-dried weight of the stems and leaves, and the air-dried weight of the ears. After the ears are threshed, weigh the air-dried grains, and weigh the air-dried weight of the grains (g). Since the roots of rice are still planted in the ground when harvested, the fertilizer element content of the roots is not calculated here.

[0062] The fertilizer element content of the stem and leaf fertilizer and the fertilizer element content of the grain fertilizer (the fertilizer element content of the rice husk + the fertilizer element content of the rice grain fertilizer) after air drying were measured. The measurement of the fertilizer element content of the stem and leaf fertilizer and the fertilizer element content of the grain fertilizer (the fertilizer element content of the rice husk + the fertilizer element content of the rice grain fertilizer) was obtained using the national standard.

[0063] Convolutional neural network models can also be used to measure the content of fertilizer elements in stems and leaves and in grains;

[0064] The specific method is:

[0065] A1: Convolutional neural network model device training process: The convolutional neural network model device is trained with the photos of stems, leaves and grains after air-drying to a specific moisture content and the corresponding crop training set of stem and leaf fertilizer element content and seed fertilizer element content to obtain the convolutional neural network model device; when constructing the training set, according to the normal distribution, several intervals are divided according to the quality of the stems, leaves or grains, and the corresponding number of samples are selected to construct the training set (that is, the number of samples close to the average value in the entire test plot is the largest, and the number of samples farther from the average value is the smaller;), the stems, leaves or grains used as the training set are complete photos, and the non-stem, leaves or grain parts of the photos are adjusted to be transparent through processing;

[0066] A2: Element content recognition process, taking photos of new unknown stem and leaf fertilizer element content and seed fertilizer element content air-dried to a specific moisture content as input, and taking the output of the convolutional neural network model as the stem and leaf fertilizer element content and seed fertilizer element content;

[0067] During the recognition process, stems and leaves or seeds whose mass conforms to the normal distribution are also selected as samples. The fertilizer element content in each identified stem, leaf or seed is summed and then divided by the corresponding weight percentage to obtain the fertilizer element content of the test plot. Since the water content during training and recognition is similar, the effect of water on the recognition results can be ignored.

[0068] It should be pointed out that the convolutional neural network models for identifying the element content of stem and leaf fertilizers and the element content of grain fertilizers should be constructed separately.

[0069] The photo size of the training set is the same as that of the air-dried stems, leaves and seeds used as the recognition procedure, and the ratio of the pixels of the photos is the same as that of the actual objects (for example, 10 pixels correspond to 1 cm of the object).

[0070] According to statistical principles and sampling ratios, the grain yield (i.e., crop yield) of the entire test plot is calculated, as well as the element content of the stem and leaf fertilizer and the element content of the grain fertilizer of the entire test plot. The amount of fertilizer used by the crop is calculated based on the proportion of the element in the fertilizer. At the same time, the training set and recognition samples are constructed based on the normal distribution of mass, which can improve the accuracy of recognition.

[0071] The larger the amount of data during training, the better, and it should be at least no less than 10,000.

[0072] The basic principle of the convolutional neural network model device is: image input → calculate features → output category.

[0073] Specifically include:

[0074] Input layer;

[0075] Feature extraction layer, each layer includes a convolution layer and a pooling layer, such as layer1-layer7, a total of 7 layers; (the convolution kernel size of the convolution layer gradually decreases, and the step size of the pooling layer is the same as the kernel size)

[0076] First fully connected layer;

[0077] The second fully connected layer;

[0078] Output layer.

[0079] The training algorithm can use common algorithms, such as stochastic gradient descent algorithm, Adam algorithm, RMSProp algorithm, Adagrad algorithm, etc.

[0080] The convolutional neural network model device consists of an input layer, several feature extraction layers, a fully connected layer, and an output layer. The parameters of each layer, such as the pooling window size, step size, convolution kernel size, number of nodes, etc., are related to the photo size of the training set. They are not the invention points of this embodiment and will not be repeated here.

[0081] The element content of stem and leaf fertilizer + the element content of seed fertilizer is taken as the total amount of crop absorption in the entire experimental plot (depending on the source area, it is divided into the total amount of crop absorption with fertilization and the total amount of crop absorption without fertilization).

[0082] Example 2

[0083] This embodiment provides an intelligent decision-making device for balanced fertilization in agriculture taking environmental impact into consideration, including:

[0084] Data acquisition module: used to obtain the initial fertilizer content Z0 of the soil in the area where crops are to be planted before cultivation, obtain the current fertilizer price P, obtain the amount of fertilizer H used during cultivation, obtain the crop yield Yk without fertilizer and the crop yield Yp with fertilizer, obtain the residual fertilizer content Z in the soil after crop harvest, obtain the unit crop fertilizer absorption content Sk without fertilizer and the unit crop fertilizer absorption content Sp with fertilizer;

[0085] Data processing module: used to calculate the total amount of crop absorption Xp after fertilization, which is the total amount of crop absorption Xp after fertilization = crop yield Yp × fertilizer absorption content Sp per unit of crop after fertilization; calculate the total amount of crop absorption Xk without fertilization, which is the total amount of crop absorption Xk without fertilization = crop yield Yk × fertilizer absorption content Sk per unit of crop without fertilization; calculate the fertilizer loss, which is the fertilizer loss S = (1-I%) × total amount of crop absorption Xp after fertilization / regional area; fertilizer utilization rate I% = (total amount of crop absorption Xp after fertilization-total amount of crop absorption Xk without fertilization-residual fertilizer content Zi in soil after harvest) / amount of fertilizer used during cultivation H × 100%;

[0086] Economic evaluation module: used to calculate the investment-production ratio D,

[0087] Among them, P represents the price of fertilizer and Py represents the price of crop products.

[0088] Preferably, in the intelligent decision-making device for balanced agricultural fertilization considering environmental impact of the present invention, the fertilizers are nitrogen fertilizer, phosphorus fertilizer and potassium fertilizer;

[0089] The calculation process of the data processing module and the economic evaluation module respectively obtains the fertilizer amount H of the three fertilizers, the residual fertilizer content Z in the soil after crop harvesting, the unit crop fertilizer absorption content Sk without fertilizer application, and the unit crop fertilizer absorption content Sp with fertilizer application; and calculates the fertilizer loss of the three fertilizers respectively;

[0090] When calculating the input-output ratio D, the three fertilizers need to be added to the formula described in S3.

[0091] Preferably, in the intelligent decision-making device for balanced agricultural fertilization considering environmental impact of the present invention, the method for obtaining the unit crop fertilizer absorption content Sp of fertilization and the unit crop fertilizer absorption content Sk of no fertilization is:

[0092] The harvested crops are air-dried, and some of the crops are randomly selected as samples using statistical methods to measure the content of fertilizer elements in the crops. Then, the unit crop fertilizer absorption content Sp for fertilization and the unit crop fertilizer absorption content Sk for no fertilization are estimated based on the sample proportions.

[0093] Preferably, in the intelligent decision-making device for balanced agricultural fertilization taking environmental impact into consideration of the present invention, the air-dried plants include crop seeds, stems, leaves, and roots, and the air-dried plants are crushed to determine the fertilizer element content.

[0094] Example 3

[0095] This embodiment provides a computer storage medium, which stores one or more instructions, and the instructions are suitable for being loaded and executed by a processor as the intelligent decision-making method for balanced agricultural fertilization considering environmental impacts as in Embodiment 1.

[0096] Based on the above ideal embodiments of this application, the relevant staff can make various changes and modifications without departing from the technical concept of this application through the above description. The technical scope of this application is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

[0097] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0098] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0099] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

Claims

1. An intelligent decision-making method for balanced agricultural fertilization considering environmental impact, characterized in that: The following steps are involved: S1: Obtain the initial soil fertilizer content Z0 of the area where crops are to be planted before cultivation, obtain the current fertilizer price P, obtain the crop yield Yk without fertilizer and the crop yield Yp with fertilizer, obtain the residual fertilizer content Z in the soil after crop harvest, obtain the unit crop fertilizer absorption content Sk without fertilizer and the unit crop fertilizer absorption content Sp with fertilizer, and take the amount of fertilizer H used during cultivation as the independent variable, H≥0; S2: Calculate the total amount of fertilizer absorbed by the crop Xp, which is the total amount of fertilizer absorbed by the crop Xp = crop yield Yp × unit crop fertilizer absorption content Sp; Calculate the total amount of crop absorption without fertilizer Xk, the total amount of crop absorption without fertilizer Xk = crop yield Yk × unit crop fertilizer absorption content Sk without fertilizer; Calculate the amount of fertilizer loss, fertilizer loss S = (1-I%) × total amount of fertilizer absorbed by crops Xp / area; fertilizer utilization rate I% = (total amount of fertilizer absorbed by crops Xp-total amount of fertilizer absorbed by crops without fertilizer Xk-residual fertilizer content in soil after harvest Zi) / amount of fertilizer used during cultivation H×100%; S3: Calculate the production ratio D curve as a function of the fertilizer amount H. Among them, P represents the price of fertilizer, and Py represents the price of crop products; S4: solving the minimum value of the input-output ratio D curve, and obtaining the value of the fertilizer amount H at this time as the optimal fertilizer amount; The convolutional neural network model was used to measure the fertilizer element content of stems and leaves and seeds after air-drying. The method comprises the following steps: A1: Convolutional neural network model device training process: The convolutional neural network model device is trained with the photos of stems, leaves and seeds air-dried to a specific moisture content and the corresponding crop training set of the fertilizer element content of the stems and leaves and the fertilizer element content of the seeds to obtain the convolutional neural network model device; when constructing the training set, according to the normal distribution, several intervals are divided according to the quality of the stems, leaves or seeds, and the corresponding number of samples are selected to construct the training set. The stems, leaves or seeds of the training set are complete photos, and the non-stem, leaves or seeds in the photos are adjusted to be transparent through processing; A2: Element content recognition process, taking photos of new unknown stem and leaf fertilizer element content and seed fertilizer element content air-dried to a specific moisture content as input, and taking the output of the convolutional neural network model as the stem and leaf fertilizer element content and seed fertilizer element content; During the identification process, stems, leaves or seeds whose mass conforms to the normal distribution are also selected as samples. The fertilizer element content in each identified stem, leaf or seed is summed up and then divided by the corresponding weight percentage to obtain the fertilizer element content of the test plot.

2. The intelligent decision-making method for balanced agricultural fertilization considering environmental impact according to claim 1 is characterized in that: The fertilizers are nitrogen fertilizer, phosphorus fertilizer and potash fertilizer; During the calculation process, the fertilizer amount H of the three fertilizers, the residual fertilizer content Z in the soil after crop harvest, the unit crop fertilizer absorption content Sk without fertilizer application, and the unit crop fertilizer absorption content Sp with fertilizer application are obtained respectively; Calculate fertilizer losses for each of the three fertilizers; When calculating the input-output ratio D, the three fertilizers need to be added to the formula described in S3.

3. An intelligent decision-making device for balanced agricultural fertilization considering environmental impact, characterized in that: include: Data acquisition module: used to obtain the initial fertilizer content Z0 of the soil in the area where crops are to be planted before cultivation, obtain the current fertilizer price P, obtain the amount of fertilizer H used during cultivation, obtain the crop yield Yk without fertilizer and the crop yield Yp with fertilizer, obtain the residual fertilizer content Z in the soil after crop harvest, obtain the unit crop fertilizer absorption content Sk without fertilizer and the unit crop fertilizer absorption content Sp with fertilizer; Data processing module: used to calculate the total amount of fertilizer absorbed by the crop Xp, which is the total amount of fertilizer absorbed by the crop Xp = crop yield Yp × unit crop fertilizer absorption content Sp; Calculate the total amount of crops absorbed without fertilizer Xk, which is Xk=crop yield Yk×unit crop fertilizer absorption content Sk without fertilizer; calculate the fertilizer loss, which is S=(1-I%)×total amount of crops absorbed with fertilizer Xp / regional area; fertilizer utilization rate I%=(total amount of crops absorbed with fertilizer Xp-total amount of crops absorbed without fertilizer Xk-residual fertilizer content in soil after harvest Zi) / amount of fertilizer used during cultivation H×100%; Economic evaluation module: used to calculate the investment-production ratio D, Among them, P represents the price of fertilizer, and Py represents the price of crop products; Optimal evaluation module: used to solve the minimum value of the input-output ratio D curve and obtain the value of the fertilizer amount H at this time as the optimal fertilizer amount; In the data acquisition module, a convolutional neural network model is used to measure the fertilizer element content of the air-dried stems and leaves and the fertilizer element content of the grains; The method comprises the following steps: A1: Convolutional neural network model device training process: The convolutional neural network model device is trained with the photos of stems, leaves and seeds air-dried to a specific moisture content and the corresponding crop training set of the fertilizer element content of the stems and leaves and the fertilizer element content of the seeds to obtain the convolutional neural network model device; when constructing the training set, according to the normal distribution, several intervals are divided according to the quality of the stems, leaves or seeds, and the corresponding number of samples are selected to construct the training set. The stems, leaves or seeds of the training set are complete photos, and the non-stem, leaves or seeds in the photos are adjusted to be transparent through processing; A2: Element content recognition process, taking photos of new unknown stem and leaf fertilizer element content and seed fertilizer element content air-dried to a specific moisture content as input, and taking the output of the convolutional neural network model as the stem and leaf fertilizer element content and seed fertilizer element content; During the identification process, stems, leaves or seeds whose mass conforms to the normal distribution are also selected as samples. The fertilizer element content in each identified stem, leaf or seed is summed up and then divided by the corresponding weight percentage to obtain the fertilizer element content of the test plot.

4. The intelligent decision-making device for balanced agricultural fertilization considering environmental impact according to claim 3 is characterized in that: The fertilizers are nitrogen fertilizer, phosphorus fertilizer and potash fertilizer; The calculation process of the data processing module and the economic evaluation module respectively obtains the fertilizer amount H of the three fertilizers, the residual fertilizer content Z in the soil after crop harvesting, the unit crop fertilizer absorption content Sk without fertilizer application, and the unit crop fertilizer absorption content Sp with fertilizer application; Calculate fertilizer losses for each of the three fertilizers; When calculating the input-output ratio D, the three fertilizers need to be added to the formula described in S3.

5. A computer storage medium, characterized in that: The computer storage medium stores one or more instructions, and the instructions are suitable for being loaded by a processor to execute the method according to claim 1 or 2.

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