Farmland crop growth state monitoring and precision agricultural fertilization system

Abstract

The invention discloses a farmland crop growth state monitoring and precision agricultural fertilization system, which comprises a sensor node module used for monitoring environment and crop growth state parameters of a plurality of positions of each region in a farmland in real time; the data processing module is used for fusing the environment and crop growth state parameters of the multiple positions, generating a comprehensive farmland environment and a comprehensive crop growth state, and extracting key growth characteristics; the growth state evaluation module is used for identifying the current growth stage of the crop according to the growth cycle and the key growth characteristics of the crop and evaluating the health condition of the crop; the nutritional demand analysis module calls a pre-established nutritional demand model based on the growth stage and the health condition of the crop, and calculates the actual nutritional demand of the crop in the current state; and the fertilization strategy generation module is used for generating a fertilization plan for crops in each region based on actual nutritional requirements. According to the invention, comprehensive monitoring and precise fertilization of the growth state of farmland crops are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of farmland management, and more particularly to a farmland crop growth status monitoring and precision agriculture fertilization system. Background Art

[0002] With the development of agricultural technology, traditional extensive agriculture is gradually transforming towards modern and intelligent farming. However, timely and accurate acquisition of crop growth status information and precise fertilization based on this information remain major challenges facing agriculture.

[0003] While existing technologies offer some solutions, they still face shortcomings in monitoring accuracy and fertilization efficiency. In particular, the growing environment within farmland is quite complex, and even within the same field, crops in different locations can exhibit significant growth differences. Monitoring only single-point environmental factors such as soil moisture, water content, light intensity, and temperature results in incomplete results. Furthermore, crops within a field typically receive the same nutrients and medications, which fails to effectively address the uneven growth and pathological conditions of crops within the field.

[0004] Therefore, how to provide a farmland crop growth status monitoring and precision agricultural fertilization system is a problem that technical personnel in this field urgently need to solve. Summary of the Invention

[0005] In view of this, the present invention provides a farmland crop growth status monitoring and precision agricultural fertilization system, which aims to achieve comprehensive monitoring of the growth status of farmland crops and precise fertilization through multi-point monitoring, comprehensive data analysis and intelligent decision support, thereby promoting the healthy growth of crops and improving yield and quality.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A farmland crop growth status monitoring and precision agriculture fertilization system, comprising:

[0008] Sensor node modules are used to monitor the environmental and crop growth parameters at multiple locations in each area of ​​the farmland in real time;

[0009] The data processing module integrates the environmental and crop growth status parameters of multiple locations to generate comprehensive farmland environment and comprehensive crop growth status, and extracts key growth characteristics;

[0010] The growth status assessment module identifies the current growth stage of the crop and assesses the health status of the crop based on the crop's growth cycle and key growth characteristics;

[0011] The nutrient requirement analysis module uses a pre-established nutrient requirement model based on the crop's growth stage and health status to calculate the crop's actual nutrient requirements in its current state.

[0012] The fertilization strategy generation module generates a fertilization plan for crops in each region based on actual nutritional needs.

[0013] Preferably, the sensor node module is provided with a plurality of sensor nodes for real-time monitoring of the environment and crop growth status parameters at a plurality of locations in each area;

[0014] Environmental and crop growth status parameters include soil moisture, soil nutrient content, light intensity, temperature, crop chlorophyll content, leaf number and plant height.

[0015] Preferably, the data processing module includes:

[0016] Data preprocessing unit: cleans and calibrates the collected environmental and crop growth status parameters;

[0017] Data fusion unit: fuses the same parameters of multiple locations after preprocessing;

[0018] Data analysis unit: perform time series analysis, correlation analysis and statistical analysis on the fused data;

[0019] Growth feature extraction unit: used to extract key growth features based on data analysis results and perform feature transformation.

[0020] Preferably, the growth status assessment module includes:

[0021] Growth cycle threshold storage unit: used to store the threshold range of growth characteristics of each growth stage;

[0022] Growth stage judgment unit: used to judge the current growth stage of the crop based on the comparison of the threshold range of each growth characteristic and the current key growth characteristic;

[0023] Health status assessment unit: Evaluates the health index of crops based on the current growth stage of the crops, environment and crop growth status parameters.

[0024] Preferably, the nutritional needs analysis module includes:

[0025] Basic nutrient requirement calculation unit: selects the corresponding growth stage coefficient according to the current growth stage of the crop to calculate the basic nutrient requirement;

[0026] Nutritional requirement adjustment unit: adjusts nutritional requirements according to basic nutritional requirements and health index to obtain adjusted nutritional requirements;

[0027] Actual nutrient requirement calculation unit: obtain the actual nutrient requirement based on the adjusted nutrient requirement and soil nutrient content.

[0028] Preferably, the basic nutritional requirement calculation formula is:

[0029]

[0030] in, Indicates basic nutritional needs, represents the growth stage coefficient, represents the standard requirement and i represents the nutrient content.

[0031] Preferably, the adjusted nutritional requirement calculation formula is:

[0032]

[0033] in, Indicates the adjusted nutritional requirements, Indicates basic nutritional needs, B indicates health index, Represents the health status coefficient.

[0034] Preferably, the actual nutritional requirement calculation formula is:

[0035]

[0036] in, Indicates actual nutritional needs, Indicates the soil nutrient content, Indicates adjusted nutritional requirements.

[0037] As can be seen from the above technical solutions, compared with the prior art, the present invention provides a farmland crop growth status monitoring and precision agricultural fertilization system, which has the following advantages:

[0038] Comprehensive monitoring: Through a multi-point sensor network, comprehensive monitoring of the environment and crop growth status at multiple locations within the farmland is achieved, improving the representativeness and accuracy of monitoring data.

[0039] Precision fertilization: Based on comprehensive data analysis and intelligent decision-making support, precise fertilization is achieved at each monitoring point, improving fertilizer utilization, reducing resource waste, and lowering environmental pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0041] Figure 1 This is a structural block diagram of a farmland crop growth status monitoring and precision agriculture fertilization system provided by the present invention.

[0042] Figure 2 This is a block diagram of the data processing module provided by the present invention.

[0043] Figure 3 This is a block diagram of the growth status assessment module provided by the present invention.

[0044] Figure 4 This is a block diagram of the nutritional needs analysis module provided by the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with 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 making creative efforts are within the scope of protection of the present invention.

[0046] The embodiment of the present invention discloses a farmland crop growth status monitoring and precision agricultural fertilization system, such as Figure 1 Shown, including:

[0047] Sensor node modules are used to monitor the environmental and crop growth parameters at multiple locations in each area of ​​the farmland in real time;

[0048] The data processing module integrates the environmental and crop growth status parameters of multiple locations to generate comprehensive farmland environment and comprehensive crop growth status, and extracts key growth characteristics;

[0049] The growth status assessment module identifies the current growth stage of the crop and assesses the health status of the crop based on the crop's growth cycle and key growth characteristics;

[0050] The nutrient requirement analysis module uses a pre-established nutrient requirement model based on the crop's growth stage and health status to calculate the crop's actual nutrient requirements in its current state.

[0051] The fertilization strategy generation module generates a fertilization plan for crops in each region based on actual nutritional needs.

[0052] In this embodiment, a plurality of sensor nodes are provided in the sensor node module, and the plurality of sensor nodes deployed in the farmland monitor the environment and crop growth status parameters at multiple locations in real time, including soil moisture, soil nutrient content, light intensity, temperature, crop chlorophyll content, number of leaves and plant height, etc.

[0053] Data transmission: Sensor nodes transmit the collected data to the data processing module via wireless or wired means. Data transmission should have high reliability and low latency to ensure the real-time and accuracy of the data.

[0054] In this embodiment, if Figure 2 As shown, the data processing module includes:

[0055] Data preprocessing unit: cleans and calibrates the collected environmental and crop growth status parameters;

[0056] Data fusion unit: fuses the same parameters of multiple locations in each area after preprocessing; for example:

[0057] Average Fusion: Take the average of the same parameters at multiple locations to generate the average value of the area.

[0058] Weighted average fusion: weighted average is performed according to sensor location and importance.

[0059] Data analysis unit: Analyze the fused data and analyze the status of crops in each area, for example:

[0060] Time series analysis: Analyze the trend of data changes over time to determine crop growth conditions and environmental changes.

[0061] Correlation analysis: Analyze the correlation between different parameters, such as the relationship between leaf color and nitrogen content.

[0062] Statistical analysis: Calculate various statistical indicators, such as mean, standard deviation, variance, etc., to reflect the changing patterns of crop growth conditions and environmental parameters.

[0063] Growth feature extraction unit: Based on the data analysis results, key crop growth features are extracted from the above fused data, such as:

[0064] Chlorophyll content: Chlorophyll content is an important indicator for evaluating crop photosynthesis capacity and growth status. Chlorophyll content varies significantly at different growth stages.

[0065] Plant height: The height of crops increases gradually over time, and the rate of height growth varies at different growth stages.

[0066] Stalk diameter: Stalk diameter can reflect the growth strength and health of the crop.

[0067] Number of leaves: The growth of the number of leaves is also an important indicator for evaluating the growth stage of crops.

[0068] Root development: The length and density of the root system can reflect the crop's ability to absorb water and nutrients.

[0069] Among them, key growth characteristics can also include newly created characteristics, such as soil water evaporation rate, photosynthesis efficiency, etc.

[0070] The extracted key growth features are transformed and the feature values ​​are normalized to the same range, such as between 0 and 1, to reduce the magnitude differences between different features. When selecting key growth features, features that are highly correlated with the growth status of the crop are selected.

[0071] In this embodiment, if Figure 3 As shown, the growth status assessment module includes:

[0072] Growth cycle threshold storage unit: In the growth status assessment, the threshold range of the key characteristic indicators of each growth stage is set. For example:

[0073] Seedling stage:

[0074] Chlorophyll content: < 30μg / cm²

[0075] Plant height: < 20 cm

[0076] Stem diameter: < 2 mm

[0077] Number of leaves: 1-3

[0078] Root development: Shallow root system, length < 10 cm

[0079] Growth period

[0080] Chlorophyll content: 30-50μg / cm²

[0081] Plant height: 20-80 cm

[0082] Stem diameter: 2-5 mm

[0083] Number of leaves: 4-10

[0084] Root development: Medium root system, length 10-30 cm

[0085] Flowering period

[0086] Chlorophyll content: >50μg / cm²

[0087] Plant height: > 80 cm

[0088] Stem diameter: 5-10 mm

[0089] Number of leaves: 10-20

[0090] Root development: well-developed root system, length > 30 cm

[0091] Maturity

[0092] Chlorophyll content: 30-50μg / cm²

[0093] Plant height: > 100 cm

[0094] Stem diameter: 10-15 mm

[0095] Number of leaves: 15-25

[0096] Root development: Very developed root system, length > 50 cm

[0097] The threshold ranges for the above key characteristic indicators need to be set according to the selected key growth characteristics. The above is just an exemplary description.

[0098] Growth stage judgment unit: compares the current key growth characteristics collected in real time with the threshold range to determine the current growth stage of the crop, such as seedling stage, growth stage, flowering stage, maturity stage, etc. For example:

[0099] Chlorophyll content: The growth stage is determined based on the threshold range of chlorophyll content.

[0100] Plant height: The growth stage is further confirmed based on the threshold range of plant height.

[0101] Stem diameter: As an auxiliary indicator, it further verifies the growth stage.

[0102] Number of leaves: As an auxiliary indicator to further verify the growth stage.

[0103] Root development: As an auxiliary indicator, it further verifies the growth stage.

[0104] Health status assessment unit: assesses the health index of crops based on the current growth stage, environment and crop growth status parameters of the crops. Specifically,

[0105] Environmental parameter assessment:

[0106] Soil moisture: The ideal range is usually between 60%-80%.

[0107] Soil nutrient content: For example, the ideal nitrogen content for corn is 100-150mg / kg.

[0108] Light intensity: The ideal range is usually 500-800 μmol / m² / s.

[0109] Temperature: The ideal range is usually 20-30 degrees Celsius.

[0110] Biological indicator assessment:

[0111] Chlorophyll content: Low chlorophyll levels may indicate crop nutritional deficiencies or pests and diseases.

[0112] Plant Height: Slow or stagnant growth in height may indicate poor growth.

[0113] Stalk diameter: Thin stem diameter may indicate nutritional deficiencies or pests and diseases.

[0114] Number of leaves: Slow growth in leaf number or symptoms such as yellowing or wilting may indicate pests, diseases or malnutrition.

[0115] Root Development: Short, sparse roots may indicate poor soil conditions or pests and diseases.

[0116] Comprehensive assessment based on environmental parameters and biological indicators:

[0117] Based on the above environmental parameters and biological indicators, the crop health index is calculated. The health index can be a comprehensive score ranging from 0 to 100, with higher values ​​indicating better crop health.

[0118] The specific calculation method is as follows:

[0119] Environmental parameter rating:

[0120] Soil moisture: Full marks will be awarded if the moisture is within the range of 60%-80%, and points will be deducted proportionally if the moisture exceeds the range.

[0121] Soil nutrient content: Full marks will be awarded if the content is within the range of 100-150 mg / kg; points will be deducted proportionally if the content exceeds the range.

[0122] Light intensity: Full marks will be awarded if the light intensity is within the range of 500-800 μmol / m² / s. Points will be deducted proportionally if the light intensity exceeds the range.

[0123] Temperature: Full marks will be awarded if the temperature is within the range of 20-30°C. Points will be deducted proportionally if the temperature exceeds the range.

[0124] Biological indicator score:

[0125] Chlorophyll content: Based on the threshold range of the growth stage, full marks will be awarded if the threshold is met, and points will be deducted proportionally if the range is exceeded.

[0126] Plant height: Based on the threshold range of the growth stage, full marks will be awarded if the threshold is met, and points will be deducted proportionally if the range is exceeded.

[0127] Stem diameter: Based on the threshold range of the growth stage, full marks will be awarded if the threshold is met, and points will be deducted proportionally if the range is exceeded.

[0128] Number of leaves: Based on the threshold range of the growth stage, full marks will be awarded if the threshold is met, and points will be deducted proportionally if the range is exceeded.

[0129] Root development: Based on the threshold range of the growth stage, full marks will be awarded if the threshold is met, and points will be deducted proportionally if the range is exceeded.

[0130] Overall rating:

[0131] The scores of all environmental parameters and biological indicators are weighted averaged to obtain the final health index.

[0132] For example, assuming the weight of environmental parameters is 0.4 and the weight of biological indicators is 0.6, the calculation formula is:

[0133] Health index = 0.4 × environmental parameter score + 0.6 × biological indicator score

[0134] Through the above method, the growth status assessment module can accurately determine the growth stage of crops and comprehensively evaluate the health status of crops, providing reliable data support for subsequent nutritional needs analysis and fertilization strategy generation.

[0135] The growth stage identification of the present invention can help determine the current physiological state of crops, which is crucial for health assessment. For example, crops in the seedling stage have different soil moisture and temperature requirements than those in the flowering stage. If a seedling crop is mistakenly identified as a flowering crop, it may lead to inappropriate management measures, thereby affecting the health of the crop.

[0136] Identifying the growth stage can also help determine the normal range of certain biological indicators. For example, the normal range of chlorophyll content is different between the seedling stage and the flowering stage. Lower chlorophyll content is normal in the seedling stage, while higher chlorophyll content is required in the flowering stage.

[0137] The present invention combines environmental parameters such as soil moisture, temperature, and light intensity, as well as biological indicators such as chlorophyll content and growth height, to assess the health of crops and identify whether there are problems such as poor growth, pests and diseases.

[0138] In this embodiment, if Figure 4 As shown, the nutrient requirement analysis module includes: a basic nutrient requirement calculation unit: selecting the corresponding growth stage coefficient according to the current growth stage of the crop to calculate the basic nutrient requirement;

[0139] Nutritional requirement adjustment unit: adjusts nutritional requirements according to basic nutritional requirements and health index to obtain adjusted nutritional requirements;

[0140] Actual nutrient requirement calculation unit: obtain the actual nutrient requirement based on the adjusted nutrient requirement and soil nutrient content.

[0141] The more specific implementation process is as follows:

[0142] 1. Model Structure

[0143] 1.1 Input Data

[0144] Growth stage: The current growth stage of the crop (such as seedling stage, growth stage, flowering stage, and maturity stage) provided by the growth status assessment module.

[0145] Health status: Crop health index (0-100) provided by the growth status assessment module.

[0146] Soil nutrient content: The content of nutrients such as nitrogen (N), phosphorus (P), and potassium (K) in the soil provided by the sensor node module.

[0147] 1.2 Output Data

[0148] Nutritional requirements: The amount of nutrients such as nitrogen (N), phosphorus (P), and potassium (K) that the crop requires in its current state.

[0149] 2. Model Building

[0150] 2.1 Pre-established nutrient requirement models

[0151] The relationship between growth stage and nutritional requirements: According to the growth stage of crops, establish nutritional requirement models for different growth stages.

[0152] Relationship between health status and nutrient requirements: Adjust nutrient requirements based on the health status of the crop. For example, a crop with a low health index may require more nutrients to recover.

[0153] 2.2 Model Parameters

[0154] Growth stage parameters: nutrient requirement coefficient for each growth stage.

[0155] Health status parameter: the influence coefficient of health index on nutritional requirements.

[0156] Soil nutrient content parameter: the coefficient of influence of existing nutrient content in the soil on demand.

[0157] 3. Calculation process

[0158] 3.1 Reading Input Data

[0159] Obtain the growth stage and health index of the crop from the growth status assessment module.

[0160] The content of nutrients such as nitrogen (N), phosphorus (P), and potassium (K) in the soil is obtained from the sensor node module.

[0161] 3.2 Calling the Nutritional Requirements Model

[0162] According to the growth stage of the crop, the corresponding nutritional requirement model is called.

[0163] 3.3 Calculating basic nutritional requirements

[0164] Basic nutritional requirements: Calculate basic nutritional requirements based on the nutritional requirement coefficient of the growth stage.

[0165]

[0166] in, Indicates basic nutritional needs, represents the growth stage coefficient, represents the standard requirement, and i represents the nutrient components, including nitrogen, phosphorus and potassium.

[0167] 3.4 Adjusting nutritional needs

[0168] Adjustment of health status: Adjust nutritional needs according to health index.

[0169]

[0170] in, Indicates the adjusted nutritional requirements, Indicates basic nutritional needs, B indicates health index, Represents the health status coefficient.

[0171] 3.5 Considering soil nutrient content

[0172] Actual nutrient requirements: Calculate actual nutrient requirements based on the existing nutrient content in the soil.

[0173]

[0174] in, Indicates actual nutritional needs, Indicates the soil nutrient content, Indicates adjusted nutritional requirements.

[0175] If the actual requirement is less than 0, the actual requirement is 0, which means that there are enough nutrients in the soil.

[0176] 4. Output results

[0177] The calculated actual demand for nutrients such as nitrogen (N), phosphorus (P), and potassium (K) is output to the fertilization strategy generation module to generate a fertilization plan for crops in each area.

[0178] A variety of machine learning and deep learning models can be used to meet the above structure. Given that the model needs to process multi-dimensional input data (growth stage, health status, soil nutrient content) and output precise nutrient requirements, a suitable choice is the Multilayer Perceptron (MLP). The MLP is a feedforward neural network capable of handling complex nonlinear relationships, making it suitable for this type of multi-input, multi-output regression task.

[0179] The multi-layer perceptron (MLP) structure can be as follows:

[0180] 1. Input layer

[0181] Input data:

[0182] Growth stage (categorical variable, can be one-hot encoded);

[0183] health status (continuous variable, 0–100);

[0184] soil nutrient content (continuous variables, N, P, K);

[0185] 2. Hidden Layer

[0186] Number of layers: usually 2-3 layers;

[0187] Number of neurons: Each layer can have 10-100 neurons, and the specific number can be adjusted according to the experiment;

[0188] Activation function: Common activation functions include ReLU, Sigmoid, Tanh, etc. ReLU usually performs better;

[0189] 3. Output Layer

[0190] Output data:

[0191] Nitrogen (N) requirements;

[0192] Phosphorus (P) requirements;

[0193] Potassium (K) requirements.

[0194] According to the actual situation of each crop, it can be obtained by targeted training based on the MLP model.

[0195] The nutrient requirement analysis module of the present invention realizes the accurate calculation of the nutrient requirements of crops and provides strong support for the development of precision agriculture.

[0196] In this embodiment, a personalized fertilization plan is generated based on the nutritional needs of the crops and the soil nutrient status. The fertilization plan should include the following:

[0197] Fertilizer type: Choose the type of fertilizer that is suitable for the current crop growth stage and nutritional needs, such as nitrogen fertilizer, phosphorus fertilizer, potassium fertilizer, etc.

[0198] Fertilizer application amount: Determine the application amount of each fertilizer based on the nutritional requirements of the crops and the nutrient status of the soil.

[0199] Fertilization time: Determine the best time to apply fertilizer based on the crop growth cycle and environmental conditions.

[0200] Fertilization location: For mobile fertilizer spreading units, determine the specific fertilizer application location and path.

[0201] Fertilization execution and feedback:

[0202] Fertilization Execution: Users review fertilization recommendations through the interactive platform. After confirming they are correct, they activate the intelligent fertilization device to begin fertilization. Fixed fertilization devices automatically adjust the type and amount of fertilizer according to the fertilization plan and apply fertilizer at a specific location. Mobile fertilization devices automatically drive to the designated location for fertilization based on the fertilization plan and navigation system.

[0203] Effect monitoring: After fertilization, continue to monitor crop growth status and soil nutrient status through sensor nodes to evaluate the fertilization effect.

[0204] Feedback adjustment: Dynamically adjust the fertilization plan based on the monitoring data of fertilization effects and optimize the fertilization strategy.

[0205] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0206] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one 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. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A farmland crop growth status monitoring and precision agriculture fertilization system, characterized in that: include: Sensor node modules are used to monitor the environmental and crop growth parameters at multiple locations in each area of ​​the farmland in real time; The data processing module integrates the environmental and crop growth status parameters of multiple locations to generate comprehensive farmland environment and comprehensive crop growth status, and extracts key growth characteristics; The growth status assessment module identifies the current growth stage of the crop and assesses the health status of the crop based on the crop's growth cycle and key growth characteristics; The nutrient requirement analysis module uses a pre-established nutrient requirement model based on the crop's growth stage and health status to calculate the crop's actual nutrient requirements in its current state. The fertilization strategy generation module generates a fertilization plan for crops in each region based on actual nutritional needs.

2. A farmland crop growth status monitoring and precision agriculture fertilization system according to claim 1, characterized in that: The sensor node module is equipped with multiple sensor nodes to monitor the environment and crop growth status parameters at multiple locations in each area in real time; Environmental and crop growth status parameters include soil moisture, soil nutrient content, light intensity, temperature, crop chlorophyll content, leaf number and plant height.

3. The farmland crop growth status monitoring and precision agriculture fertilization system according to claim 1, characterized in that: The data processing module includes: Data preprocessing unit: cleans and calibrates the collected environmental and crop growth status parameters; Data fusion unit: fuses the same parameters of multiple locations after preprocessing; Data analysis unit: perform time series analysis, correlation analysis and statistical analysis on the fused data; Growth feature extraction unit: used to extract key growth features based on data analysis results and perform feature transformation.

4. The farmland crop growth status monitoring and precision agriculture fertilization system according to claim 2, characterized in that: The growth status assessment module includes: Growth cycle threshold storage unit: used to store the threshold range of growth characteristics of each growth stage; Growth stage judgment unit: used to judge the current growth stage of the crop based on the comparison of the threshold range of each growth characteristic and the current key growth characteristic; Health status assessment unit: Evaluates the health index of crops based on the current growth stage of the crops, environment and crop growth status parameters.

5. The farmland crop growth status monitoring and precision agriculture fertilization system according to claim 4, characterized in that: The Nutritional Requirements Analysis module includes: Basic nutrient requirement calculation unit: selects the corresponding growth stage coefficient according to the current growth stage of the crop to calculate the basic nutrient requirement; Nutritional requirement adjustment unit: adjusts nutritional requirements according to basic nutritional requirements and health index to obtain adjusted nutritional requirements; Actual nutrient requirement calculation unit: obtain the actual nutrient requirement based on the adjusted nutrient requirement and soil nutrient content.

6. The farmland crop growth status monitoring and precision agriculture fertilization system according to claim 5, characterized in that: The basic nutritional requirements calculation formula is: ； in, Indicates basic nutritional needs, represents the growth stage coefficient, represents the standard requirement and i represents the nutrient content.

7. The farmland crop growth status monitoring and precision agriculture fertilization system according to claim 5, characterized in that: The formula for calculating the adjusted nutritional requirements is: ； in, Indicates the adjusted nutritional requirements, Indicates basic nutritional needs, B indicates health index, Represents the health status coefficient.

8. The farmland crop growth status monitoring and precision agriculture fertilization system according to claim 5, characterized in that: The actual nutritional requirements are calculated as follows: ； in, Indicates actual nutritional needs, Indicates the soil nutrient content, Indicates adjusted nutritional requirements.

Images