Ground-air integrated agricultural detection system and method

Abstract

The invention discloses a ground-air integrated agricultural detection system and method, and relates to the technical field of agricultural detection. Farmland multi-dimensional data are collected through the ground-air sensor, and the farmland environment is comprehensively monitored. Based on the soil moisture dynamic change and the crop root activity data, obtaining a root moisture utilization efficiency index; identifying crop disease symptoms and pest population distribution, predicting a pest diffusion trend, and obtaining a pest propagation risk index; the soil erosion risk index is obtained by analyzing the soil particle loss, the surface runoff intensity and the vegetation coverage change. The early warning module comprehensively evaluates the root system moisture utilization efficiency index, the disease and pest propagation risk index and the soil erosion risk index to obtain a farmland detection index, an early warning mechanism is set, and early warning information is generated when the farmland detection index triggers an early warning condition. Farmland conditions can be monitored in real time, a scientific management basis is provided, the intelligent level of agricultural management is improved, and resource waste and crop loss are reduced.

Description

Technical Field

[0001] The present invention relates to the field of agricultural detection technology, and in particular to a ground-to-air integrated agricultural detection system and method. Background Art

[0002] With the rapid growth of the global population and the continuous advancement of urbanization, agriculture is facing tremendous production pressure. With limited land resources, farmers not only have to meet the growing demand for food production, but also have to cope with many challenges such as climate change, environmental degradation, and overuse of resources. Traditional agricultural management methods rely too much on experience judgment and manual detection, which is not only inefficient, but also easily affected by environmental factors, making it difficult to adapt to the refined requirements of modern agriculture. At the same time, with the development of agricultural technology, intelligent, data-driven refined agriculture has gradually become an important direction of agricultural modernization, especially in dealing with the effective use of natural resources and the sustainable management of the agricultural environment.

[0003] Traditional agricultural detection systems usually rely on manual observation and a single ground sensor device, lacking comprehensive data support. This method is not only inefficient and time-consuming, but also due to human factors, the detection results are easily affected by subjectivity, making it difficult to ensure the accuracy of the data. In addition, a single ground detection system cannot fully cover a large area of ​​farmland, resulting in incomplete monitoring of crop growth conditions, pests and diseases, and soil erosion. At the same time, traditional systems lack the ability to comprehensively analyze multi-dimensional data and cannot promptly warn of potential risks, resulting in more serious problems such as the spread of crop pests and diseases and waste of water resources. Faced with the demand for precise monitoring and intelligent management in modern agriculture, traditional systems have been unable to meet the requirements of practical applications. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a ground-to-air integrated agricultural detection system, which solves the problems of the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a ground-to-air integrated agricultural detection system and method, comprising the following modules: a data acquisition module, an agricultural detection module, and an early warning module; the data acquisition module is used to acquire multi-dimensional data of farmland through ground-to-air sensors; the agricultural detection module includes an irrigation detection unit, a pest and disease detection unit, and a soil erosion detection unit; the irrigation detection unit is used to analyze the dynamic changes of soil moisture and crop root vitality based on the multi-dimensional data of farmland, and obtain the root water use efficiency index; the pest and disease detection unit is used to identify crop disease symptoms and pest population distribution based on the multi-dimensional data of farmland, predict the spread trend of pests and diseases, and obtain the pest and disease transmission risk index; the soil erosion detection unit is used to comprehensively analyze the soil particle loss, surface runoff intensity and vegetation cover changes based on the multi-dimensional data of farmland, and obtain the soil erosion risk index; the early warning module is used to comprehensively evaluate the root water use efficiency index, the pest and disease transmission risk index and the soil erosion risk index, obtain the farmland detection index, set an early warning mechanism, and generate early warning information when the farmland detection index triggers the early warning mechanism.

[0006] Furthermore, the multi-dimensional farmland data includes ground farmland data and aerial farmland data; the ground farmland data includes soil moisture sensor data at different depths, crop root vitality data of root sensors, disease sensor data and environmental monitoring sensor data; the aerial farmland data includes farmland remote sensing image data.

[0007] Furthermore, the dynamic changes of soil moisture and crop root activity are analyzed to obtain the root water use efficiency index. The specific process is as follows: based on the data of soil moisture sensors at different depths, the dynamic change trend of soil moisture is analyzed to identify the relationship between water demand and water supply capacity during the growth stage; based on the crop root activity data of the root sensor, the changes in crop root activity data are analyzed, including the growth rate, density distribution and physiological characteristics of the roots, the health status of the roots under different environmental conditions and their ability to absorb water are evaluated, and the root water use efficiency index is obtained in combination with the dynamic changes of soil moisture.

[0008] Furthermore, the specific process of identifying crop disease symptoms and pest population distribution and predicting the spread trend of pests and diseases is as follows: based on farmland remote sensing image data and disease sensor data, identify the symptoms of crop diseases, capture the distribution of pest populations in farmland, and cross-validate with ground data; extract the key features of pest and disease transmission through statistical analysis, build a pest and disease spread prediction model, analyze the impact of environmental factors on pest and disease transmission, and generate a pest and disease spread trend map.

[0009] Furthermore, the specific process of obtaining the pest and disease transmission risk index is as follows: based on the identified pest and disease symptoms and pest population distribution, risk factor analysis is conducted in combination with environmental factors to determine the key variables that affect pest and disease transmission; based on the pest and disease spread prediction model, combined with the key variables of pest and disease transmission, the pest and disease transmission risk index is obtained.

[0010] Furthermore, a comprehensive analysis was conducted on soil particle loss, surface runoff intensity and vegetation cover changes. The specific process of obtaining the soil erosion risk index is as follows: based on the environmental monitoring sensor data, which includes soil particle loss sensor data, surface runoff monitoring data and vegetation cover sensor data, the rate and pattern of soil particle loss were analyzed, and the potential impact of soil erosion was evaluated in combination with the surface runoff intensity and vegetation cover changes to obtain the soil erosion risk index.

[0011] Furthermore, the specific process of obtaining the farmland detection index is as follows: weights are assigned to the root water use efficiency index, pest and disease transmission risk index, and soil erosion risk index through the analytic hierarchy process, and a comprehensive analysis is performed to obtain the farmland detection index.

[0012] A ground-to-air integrated agricultural detection method comprises the following steps: S1. acquiring multi-dimensional data of farmland through ground-to-air sensors; S2. analyzing the dynamic changes of soil moisture and the activity of crop roots based on the multi-dimensional data of farmland, and acquiring the root water use efficiency index; S3. identifying the symptoms of crop diseases and the distribution of pest populations based on the multi-dimensional data of farmland, predicting the spread trend of pests and diseases, and acquiring the pest and disease transmission risk index; S4. comprehensively analyzing the soil particle loss, surface runoff intensity and vegetation coverage changes based on the multi-dimensional data of farmland, and acquiring the soil erosion risk index; S5. comprehensively evaluating the root water use efficiency index, the pest and disease transmission risk index and the soil erosion risk index, acquiring the farmland detection index, setting an early warning mechanism, and generating early warning information when the farmland detection index triggers the early warning mechanism.

[0013] The present invention has the following beneficial effects: (1) This ground-to-air integrated agricultural detection system integrates ground and air sensors through a data acquisition module, and can obtain multi-dimensional data of farmland, realizing comprehensive monitoring of the farmland environment, overcoming the problem of incomplete data and insufficient accuracy caused by traditional systems relying on a single sensor or manual detection. Based on soil moisture and crop root vitality data, it can analyze the dynamic change trend of soil moisture and obtain the root water use efficiency index, helping agricultural managers optimize irrigation strategies, effectively save water resources, and improve irrigation efficiency. Using remote sensing images and sensor data, it can accurately identify crop disease symptoms and the distribution of pest populations, and predict the spread trend of pests and diseases based on environmental factors, generate a pest and disease transmission risk index, thereby achieving early warning and reducing economic losses caused by pests and diseases.

[0014] (2) This ground-to-air integrated agricultural detection method can comprehensively evaluate soil erosion and generate a soil erosion risk index by monitoring soil particle loss, surface runoff intensity, and vegetation cover changes, which helps to take effective measures to prevent soil degradation and protect the agricultural ecological environment. A comprehensive evaluation of multiple indexes is performed to generate a farmland detection index, and an early warning mechanism is set up to promptly detect potential problems in the farmland. When the monitoring results trigger the early warning mechanism, the system can quickly generate early warning information to help managers take corresponding measures and improve the level of intelligent agricultural management.

[0015] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a ground-to-air integrated agricultural detection system of the present invention.

[0017] Figure 2 The present invention is a flow chart of a ground-to-air integrated agricultural detection method. DETAILED DESCRIPTION

[0018] The embodiment of the present application solves the problem that traditional agricultural detection methods are unable to monitor and warn of changes in farmland environment in real time and comprehensively through a ground-to-air integrated agricultural detection system.

[0019] The overall idea of ​​the problem in the embodiment of this application is as follows: Obtain multi-dimensional farmland data through ground and air sensors.

[0020] Based on the multi-dimensional data of farmland, the dynamic changes of soil moisture and crop root vitality are analyzed to obtain the root water use efficiency index. Based on the multi-dimensional data of farmland, the symptoms of crop diseases and the distribution of pest populations are identified, the spread trend of pests and diseases is predicted, and the pest and disease transmission risk index is obtained.

[0021] Based on multi-dimensional data of farmland, a comprehensive analysis is conducted on soil particle loss, surface runoff intensity and vegetation cover changes to obtain the soil erosion risk index.

[0022] Conduct a comprehensive assessment of the root water use efficiency index, pest and disease transmission risk index, and soil erosion risk index, obtain the farmland detection index, set up an early warning mechanism, and generate early warning information when the farmland detection index triggers the early warning mechanism.

[0023] See also Figure 1 The embodiment of the present invention provides a technical solution: a ground-to-air integrated agricultural detection system and method, comprising the following modules: a data acquisition module, an agricultural detection module, and an early warning module; the data acquisition module is used to acquire multi-dimensional data of farmland through ground-to-air sensors; the agricultural detection module includes an irrigation detection unit, a pest and disease detection unit, and a soil erosion detection unit; the irrigation detection unit is used to analyze the dynamic changes of soil moisture and crop root vitality based on the multi-dimensional data of farmland, and obtain the root water use efficiency index; the pest and disease detection unit is used to identify crop disease symptoms and pest population distribution based on the multi-dimensional data of farmland, predict the spread trend of pests and diseases, and obtain the pest and disease transmission risk index; the soil erosion detection unit is used to comprehensively analyze the soil particle loss, surface runoff intensity and vegetation coverage changes based on the multi-dimensional data of farmland, and obtain the soil erosion risk index; the early warning module is used to comprehensively evaluate the root water use efficiency index, the pest and disease transmission risk index and the soil erosion risk index, obtain the farmland detection index, set an early warning mechanism, and generate early warning information when the farmland detection index triggers the early warning mechanism.

[0024] In this implementation scheme, data acquisition module: This module collects multi-dimensional data of farmland through ground and aerial sensors. Ground-to-air sensors refer to a variety of sensor devices installed on the ground and in the air, which can obtain environmental data at different levels and angles, including soil moisture and root activity data on the ground, and farmland image information obtained through aerial remote sensing. This integration of multi-dimensional data enables the farmland environment to be monitored more comprehensively. Irrigation detection unit: This unit calculates the root water use efficiency index by analyzing the dynamic changes in soil moisture and the vitality of crop roots. The root water use efficiency index is used to evaluate the ability of crops to absorb and utilize water at different growth stages, helping to optimize irrigation strategies.

[0025] Pest and disease detection unit: This unit identifies crop disease symptoms and pest population distribution in farmland based on multi-dimensional data, further predicts the spread trend of pests and diseases, and generates a pest and disease transmission risk index. This index is used to measure the risk of pest and disease transmission in farmland and help take preventive measures. Soil erosion detection unit: Through comprehensive analysis of soil particle loss, surface runoff intensity and vegetation cover changes, the soil erosion risk index is obtained to assess the risk of soil erosion to prevent soil fertility from declining. Early warning module: This module generates a farmland detection index by comprehensively evaluating the root water use efficiency index, pest and disease transmission risk index and soil erosion risk index. When the index reaches the early warning threshold, the system triggers the early warning mechanism and sends an early warning message to the user, reminding him to take corresponding management measures. Root water use efficiency index: This is a parameter used to measure the efficiency of crop roots in absorbing and using soil water. Through sensor data (such as soil moisture sensors and root activity sensors), the system can analyze the moisture change trend at different depths of the soil and the absorption capacity of crop roots. The root water use efficiency index helps assess the water demand and absorption of crops at different growth stages, optimize irrigation strategies, avoid water waste or shortage, and thus improve crop yields and resource utilization. Pest and disease transmission risk index: This index is used to measure the risk of the occurrence and spread of pests and diseases in farmland. The system identifies the current status of crop diseases and pests by analyzing multi-dimensional farmland data, including disease sensors, pest sensors, and remote sensing image data, and predicts the spread trend of pests and diseases based on historical data. The pest and disease transmission risk index helps farmers take preventive measures in a timely manner to avoid farmland losses caused by the spread of pests and diseases. Soil erosion risk index: This is an indicator used to assess the risk of soil erosion in farmland. Soil erosion refers to the loss of soil particles due to external forces such as wind and rain, resulting in a decrease in soil fertility. By analyzing soil particle loss sensor data, surface runoff monitoring data, and vegetation cover sensor data, the system can assess the speed and pattern of soil loss in farmland and calculate the soil erosion risk index. This index is used to help agricultural managers take measures to reduce soil loss and maintain soil fertility. Farmland Detection Index: The Farmland Detection Index is a comprehensive index generated by comprehensively analyzing multiple agricultural environmental parameters. It provides a comprehensive assessment of the overall condition of farmland by integrating the root water use efficiency index, pest and disease transmission risk index, and soil erosion risk index. When the farmland detection index exceeds a certain preset threshold, the system will automatically trigger an early warning mechanism to notify managers to take corresponding measures.

[0026] Specifically, multi-dimensional farmland data include ground farmland data and aerial farmland data; ground farmland data include soil moisture sensor data at different depths, crop root vitality data from root sensors, disease sensor data and environmental monitoring sensor data; aerial farmland data include farmland remote sensing image data.

[0027] In this implementation scheme, ground farmland data: ground farmland data is collected in real time through various sensors installed on the ground or in the soil, specifically including the following categories: soil moisture sensor data at different depths: these sensors are installed at different depths in the farmland soil to monitor the changes in moisture content in each layer of the soil. This data can help agricultural managers accurately understand the moisture status of the soil to optimize irrigation plans. Crop root vitality data of root sensors: root sensors are used to monitor the growth vitality and absorption capacity of crop roots. By measuring indicators such as the water absorption efficiency and nutrient absorption capacity of the roots, managers can understand the health of the crop roots and adjust agricultural operations such as fertilization and irrigation. Disease sensor data: disease sensors are used to detect the disease status of crops, including leaf spots, pathogenic bacteria infection, etc. This data can help identify early diseases, so as to take preventive or therapeutic measures to avoid the spread of diseases. Environmental monitoring sensor data: these sensors are used to monitor key factors of the farmland environment, such as temperature, humidity, wind speed, rainfall, etc. Environmental monitoring data is of great significance for understanding crop growth conditions and soil erosion risks, helping agricultural managers make accurate decisions.

[0028] Aerial farmland data: Aerial farmland data is remote sensing information of farmland obtained through aerial platforms (such as drones or satellites), specifically including: Farmland remote sensing image data: This is farmland images taken by drones or satellite remote sensing technology, covering information such as the health of crops, vegetation coverage, and soil moisture distribution. Remote sensing image data can provide an overall perspective of large areas of farmland, making it easy to quickly discover problem areas such as crop diseases or uneven moisture. Explanation summary: Ground farmland data is mainly obtained through sensors installed inside the farmland, focusing on soil moisture, root vitality, disease detection, and environmental factors. Aerial farmland data uses remote sensing images of farmland taken by drones or satellites to provide a global perspective of crop health and soil cover information.

[0029] Specifically, the specific process of analyzing the dynamic changes of soil moisture and crop root activity to obtain the root water use efficiency index is as follows: based on the data of soil moisture sensors at different depths, the dynamic change trend of soil moisture is analyzed to identify the relationship between water demand and water supply capacity during the growth stage; based on the crop root activity data of the root sensor, the changes in crop root activity data are analyzed, including the growth rate, density distribution and physiological characteristics of the roots, the health status of the roots under different environmental conditions and their ability to absorb water are evaluated, and combined with the dynamic changes of soil moisture, the root water use efficiency index is obtained.

[0030] In this implementation scheme, analysis of dynamic changes in soil moisture: soil moisture sensors at different depths are used to monitor the moisture content of each layer in the soil in real time. Based on these data, the trend of soil moisture changes over time is analyzed to identify changes in crop water demand at different growth stages. At the same time, the information provided by the sensor is used to evaluate the current water supply capacity of the soil and determine whether the water supply capacity meets the water requirements of the crops. Root activity analysis: Root sensors are used to obtain data on crop root activity, including root growth rate, density distribution, and physiological characteristics (such as the ability of roots to absorb water and nutrients). This information reflects the health of crop roots, especially their performance under different environmental conditions, such as the effects of adverse conditions such as high temperature and drought on root health. Calculation of root water use efficiency index: The root water use efficiency index ( ). This index reflects the efficiency of crop roots in absorbing and utilizing water under current water conditions. The calculation formula is: ;in: Indicates The vitality index of the root system in the soil layer covers the growth rate, density and physiological health of the root system; Indicates The water absorption rate of a layer of soil refers to the proportion of water absorbed by the roots in this layer of soil; It represents the total available water content of the soil in the entire farmland, that is, the summary value of soil water content at different depths. Represents the number of soil layers, from layer 1 to layer The soil layers represent the soil profile at different depths. Each layer of soil has different moisture and root activity, so the data of each layer is analyzed separately. It indicates the total number of soil stratification layers in the farmland, that is, how many depth levels the soil is divided into, and the moisture and root conditions of each layer are different. : The root vitality index in the soil collected by the root sensor mainly reflects the health status, density and ability to absorb water of the root system at a specific depth. The higher the root vitality, the healthier the root growth and the stronger the ability to utilize water. The calculation formula of the root vitality index in the soil is: ;in: Indicates The root activity index in the soil layer, Indicates The relative growth rate of the root system in the layer is used to evaluate the growth rate of the root system at a specific depth; Indicates The root density in the soil layer is the density of the roots in the soil layer. Indicates The physiological health status of the root system is measured by collecting the physiological characteristics of the root system through the root system sensor, such as the root respiration rate and enzyme activity parameters; , and is a weight coefficient used to adjust the influence of relative growth rate, root density and physiological health status on root vitality index according to actual conditions. These weights can be determined by experimental data.

[0031] Specifically, the specific process of identifying crop disease symptoms and pest population distribution and predicting the spread trend of pests and diseases is as follows: based on farmland remote sensing image data and disease sensor data, identify the symptoms of crop diseases, capture the distribution of pest populations in farmland, and cross-validate with ground data; extract the key features of pest and disease transmission through statistical analysis, build a pest and disease spread prediction model, analyze the impact of environmental factors on pest and disease transmission, and generate a pest and disease spread trend map.

[0032] In this implementation scheme, based on farmland remote sensing image data: remote sensing equipment such as drones or satellites are used to obtain high-resolution images of farmland, and crop disease symptoms are identified through image processing technology. Remote sensing images can provide a wide-area perspective, which helps to find disease spots or abnormal crop areas in farmland. Disease sensor data: disease sensors on the ground are used to detect pathological changes in crops in real time and obtain physiological data of crop diseases, such as leaf color changes, disease spot distribution, etc. These data can be cross-validated with remote sensing image data to improve the accuracy of recognition. Capture of pest population distribution: capture the population distribution of different pests in farmland through visual analysis and sensor data, and identify areas with dense pests. The pest data captured by ground sensors combined with abnormal areas in remote sensing images can help identify concentrated areas of pests. Feature extraction: By statistically analyzing remote sensing data and disease sensor data, key features related to the spread of pests and diseases are extracted, such as the spread rate of diseases, the activity range of pest populations, and the health status of crops. Cross-validation: Use cross-validation between ground sensor data and remote sensing image data to ensure that the identification results of crop disease symptoms and pest distribution are accurate and reliable. This can avoid misjudgment caused by relying solely on a single data source. Prediction model construction: Based on the pest and disease transmission characteristics extracted in the early stage, a mathematical model or machine learning model is constructed to predict the spread trend of pests and diseases. This model usually takes into account the spatial distribution, temporal changes and environmental factors of pests and diseases. Environmental factor analysis: Analyze the impact of environmental factors in farmland on the spread of pests and diseases. Environmental factors are often the key driving force for the spread of pests and diseases, so these variables must be included in the model to improve the prediction accuracy of the model. Trend chart generation: Based on the output results of the prediction model, a visual pest and disease spread trend chart is generated to show the areas and ranges where pests and diseases may spread in a certain period of time in the future. Trend charts can help farmers and agricultural managers take preventive measures in time to reduce losses caused by pests and diseases.

[0033] Specifically, the specific process of obtaining the pest and disease transmission risk index is as follows: Based on the identified pest and disease symptoms and pest population distribution, risk factor analysis is conducted in combination with environmental factors to determine the key variables that affect the spread of pests and diseases; based on the pest and disease spread prediction model, combined with the key variables of pest and disease transmission, the pest and disease transmission risk index is obtained.

[0034] In this implementation plan, pest and disease symptom identification: the pest and disease symptoms identified through remote sensing images and pest sensor data are used to determine the health status of crops and the population distribution of pests. These data provide the basis for analysis. Environmental factor considerations: Risk factor analysis of pest and disease transmission is conducted in combination with environmental factors (such as temperature, humidity, precipitation, light intensity, etc.). These environmental factors play an important role in the growth, reproduction, and spread of pests and diseases. Determination of key variables: Through statistical analysis and correlation studies, the variables that have the greatest impact on the spread of pests and diseases are identified, including ambient temperature, air humidity, precipitation, and light intensity. The pest and disease diffusion prediction model constructed in the early stage is used. The model uses key variables as input to calculate the spread trend of pests and diseases. Pest and disease transmission risk index ( ) The formula is: ; : Pest and disease transmission risk index, the higher the value, the greater the risk, and vice versa, the smaller the risk. : The weight coefficient of each environmental factor reflects the relative impact of different factors on the risk of pest and disease transmission. : Ambient temperature, usually expressed in degrees Celsius. Increased temperatures may promote the growth and spread of pests and diseases. : Air humidity, expressed as a percentage. Too high or too low humidity may affect the occurrence of pests and diseases. : Precipitation, in millimeters. Precipitation affects soil moisture and the breeding environment of pests and diseases. : Light intensity, usually measured in Lux, affects the growth of crops and their ability to resist pests and diseases. : Weight coefficients determined based on historical data or experiments, used to quantify the contribution of each variable to the risk of pest and disease transmission.

[0035] Specifically, a comprehensive analysis is conducted on soil particle loss, surface runoff intensity and vegetation cover changes. The specific process of obtaining the soil erosion risk index is as follows: Based on environmental monitoring sensor data, which includes soil particle loss sensor data, surface runoff monitoring data and vegetation cover sensor data, the rate and pattern of soil particle loss are analyzed, and the potential impact of soil erosion is evaluated in combination with surface runoff intensity and vegetation cover changes to obtain the soil erosion risk index.

[0036] In this implementation plan, the following data are obtained through environmental monitoring sensors: Soil particle loss sensor data: monitor the loss of particles in the soil, including the loss rate and loss amount. Surface runoff monitoring data: measure the flow intensity and flow of surface water after rainfall, and evaluate the erosion potential of water flow on the soil. Vegetation cover sensor data: evaluate the coverage of vegetation in farmland, and vegetation can effectively reduce soil erosion. Soil particle loss rate and pattern analysis: through the data of soil particle loss sensor, analyze the loss rate (unit: kg / m²·h) and loss pattern of soil particles, and identify the temporal and spatial distribution characteristics of soil loss. Combining surface runoff intensity with vegetation cover changes: analyze surface runoff intensity (unit: m³ / s) and vegetation cover changes (unit: %), and evaluate the potential impact of these two factors on soil erosion. High-intensity runoff and low vegetation cover will aggravate soil erosion. Soil erosion risk assessment: comprehensively analyze the soil particle loss, surface runoff intensity and vegetation cover changes, evaluate the risk level of soil erosion, and obtain the soil erosion risk index. The calculation formula for the soil erosion risk index is: ;in: : Soil erosion risk index, the higher the value, the greater the soil erosion risk. : Soil particle loss rate (unit: kg / m2 h), reflects the severity of soil loss. :Surface runoff intensity (unit: m 3 / s), indicating the intensity of surface water flow after rainfall, affecting the possibility of soil erosion, : Vegetation coverage rate (unit: %), vegetation coverage can effectively reduce soil erosion, and the higher the value, the lower the erosion risk. : The weight coefficient of each factor reflects the relative impact of different factors on soil erosion risk. : Soil erosion risk index, indicating the potential risk of soil erosion. The higher the value, the greater the erosion risk. The soil particle loss rate indicates the number of soil particles lost per unit time. The larger the rate, the more serious the loss. : Surface runoff intensity refers to the amount of water flowing over the surface after precipitation. The greater the flow, the higher the risk of soil erosion. : Vegetation coverage rate indicates the coverage of vegetation in farmland. The higher the value, the denser the vegetation, which helps to reduce soil erosion. : Weight coefficient, determined based on historical data or expert judgment, quantifies the contribution of each variable to soil erosion risk. The above formula and its parameters can be used to scientifically assess soil erosion risk, provide a basis for farmland management, and help agricultural managers take effective prevention and control measures.

[0037] Specifically, the specific process of obtaining the farmland detection index is as follows: weights are assigned to the root water use efficiency index, pest and disease transmission risk index and soil erosion risk index through the hierarchical analysis method, and a comprehensive analysis is performed to obtain the farmland detection index.

[0038] In this implementation scheme, weights are assigned to each indicator through the analytic hierarchy process. AHP is a decision support method that evaluates the importance of each indicator by constructing a hierarchy and performing pairwise comparisons to determine their respective weights. Pairwise comparisons are performed to obtain the relative importance of each indicator, and the farmland monitoring index (FMI) is calculated using the following formula: ;in, The farmland detection index comprehensively evaluates the health status and risk level of farmland. The higher the value, the better the health status of the farmland and the lower the risk level. The root water use efficiency index reflects the root system's ability to absorb water. The higher the value, the higher the water use efficiency. : Pest and disease transmission risk index, which indicates the risk level of pest and disease transmission. The higher the value, the greater the risk. : Soil erosion risk index, which assesses the risk of soil erosion. The higher the value, the greater the risk of soil erosion. : are the weight coefficients of root water use efficiency index, pest transmission risk index and soil erosion risk index, respectively, and meet the following conditions: .

[0039] See also Figure 2 , a ground-to-air integrated agricultural detection method, comprising the following steps: S1. Acquire multi-dimensional data of farmland through ground-to-air sensors; S2. Analyze the dynamic changes of soil moisture and crop root vitality based on the multi-dimensional data of farmland, and obtain the root water use efficiency index; S3. Identify crop disease symptoms and pest population distribution based on the multi-dimensional data of farmland, predict the spread trend of pests and diseases, and obtain the pest and disease transmission risk index; S4. Based on the multi-dimensional data of farmland, conduct a comprehensive analysis of soil particle loss, surface runoff intensity and vegetation coverage changes to obtain the soil erosion risk index; S5. Comprehensively evaluate the root water use efficiency index, pest and disease transmission risk index and soil erosion risk index, obtain the farmland detection index, set an early warning mechanism, and generate early warning information when the farmland detection index triggers the early warning mechanism.

[0040] In this implementation scheme, S1. Multi-dimensional data of farmland is obtained through ground-to-air sensors. In this stage, multi-dimensional data about farmland is collected through sensors deployed on the ground and in the air. These data include information such as soil moisture, climate conditions, and crop growth status. The combination of ground-to-air sensors makes the acquired data more comprehensive and can provide a basis for subsequent analysis. S2. Based on the multi-dimensional data of farmland, the dynamic changes of soil moisture and the root vitality of crops are analyzed to obtain the root water use efficiency index. In this step, the collected soil moisture sensor data and root vitality sensor data are used to analyze the changing trend of soil moisture and its relationship with the root vitality of crops. Through this analysis, the root water use efficiency index (RWUEI) is calculated to evaluate the water absorption capacity and utilization efficiency of crops at different growth stages. S3. Based on the multi-dimensional data of farmland, the symptoms of crop diseases and the distribution of pest populations are identified, the spread trend of pests and diseases is predicted, and the pest and disease transmission risk index is obtained. This step uses farmland remote sensing image data and disease sensor data to identify the symptoms of crop diseases and the distribution of pests and diseases. At the same time, combined with environmental factors, a pest and disease spread prediction model is constructed through statistical analysis, and the pest and disease transmission risk index (RPI) is calculated to provide pest and disease risk assessment for agricultural managers. S4. Based on the multi-dimensional data of farmland, a comprehensive analysis of soil particle loss, surface runoff intensity and vegetation cover changes is carried out to obtain the soil erosion risk index. In this stage, the environmental monitoring sensor data is used to analyze the changes in soil particle loss, surface runoff and vegetation cover. Through comprehensive evaluation, the soil erosion risk index (SERI) is calculated to determine the potential risk of soil erosion and help take appropriate prevention and control measures. S5. A comprehensive evaluation is carried out on the root water use efficiency index, pest and disease transmission risk index and soil erosion risk index to obtain the farmland detection index, set up an early warning mechanism, and generate early warning information when the farmland detection index triggers the early warning mechanism. Finally, the three key indicators (RWUEI, RPI and SERI) are comprehensively evaluated through the hierarchical analysis method to calculate the farmland detection index (FMI). At the same time, an early warning mechanism is set up to automatically generate early warning information when the farmland detection index reaches the predetermined threshold, and promptly notify farmers or managers to take measures. This process can improve the sustainability of agricultural production and reduce potential losses.

[0041] In summary, this application has at least the following effects: A ground-to-air integrated agricultural detection system and method. The ground-to-air sensors are used to collect multi-dimensional data of farmland and comprehensively monitor the farmland environment. Based on the dynamic changes of soil moisture and the root vitality data of crops, the root water use efficiency index is obtained; the crop disease symptoms and the distribution of pest populations are identified, the pest spread trend is predicted, and the pest transmission risk index is obtained; the soil erosion risk index is obtained by analyzing the loss of soil particles, the surface runoff intensity and the vegetation coverage changes. The root water use efficiency index, the pest transmission risk index and the soil erosion risk index are comprehensively evaluated through the early warning module to obtain the farmland detection index, and the early warning mechanism is set to generate early warning information when the farmland detection index triggers the early warning condition. It can monitor the farmland status in real time and provide a scientific management basis, improve the intelligent level of agricultural management, and reduce resource waste and crop losses.

[0042] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0043] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0044] 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.

[0045] 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 The steps for the functions specified in one or more boxes.

[0046] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0047] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A ground-air integrated agricultural detection system, characterized in that: It includes the following modules: data acquisition module, agricultural detection module, and early warning module; The data acquisition module is used to acquire multi-dimensional data of farmland through ground-to-air sensors; The agricultural detection module includes an irrigation detection unit, a pest detection unit, and a soil erosion detection unit; The irrigation detection unit is used to analyze the dynamic changes of soil moisture and crop root activity based on multi-dimensional farmland data to obtain a root water use efficiency index; The pest detection unit is used to identify crop disease symptoms and pest population distribution based on multi-dimensional farmland data, predict the spread trend of pests and diseases, and obtain the pest and disease transmission risk index; The soil erosion detection unit is used to comprehensively analyze soil particle loss, surface runoff intensity and vegetation cover changes based on multi-dimensional farmland data to obtain a soil erosion risk index; The early warning module is used to comprehensively evaluate the root water use efficiency index, pest and disease transmission risk index and soil erosion risk index, obtain the farmland detection index, set up an early warning mechanism, and generate early warning information when the farmland detection index triggers the early warning mechanism.

2. The ground-to-air integrated agricultural detection system according to claim 1, characterized in that: The multi-dimensional farmland data includes ground farmland data and aerial farmland data; The ground farmland data includes soil moisture sensor data at different depths, crop root vitality data from root sensors, disease sensor data, and environmental monitoring sensor data; The aerial farmland data includes farmland remote sensing image data.

3. The ground-to-air integrated agricultural detection system according to claim 2, characterized in that: The specific process of analyzing the dynamic changes of soil moisture and crop root activity and obtaining the root water use efficiency index is as follows: Based on the data from soil moisture sensors at different depths, the dynamic trend of soil moisture is analyzed to identify the relationship between water demand and water supply capacity during the growth stage; Based on the crop root vitality data of root sensors, the changes in crop root vitality data are analyzed, including the growth rate, density distribution and physiological characteristics of the roots, the health status of the roots under different environmental conditions and their ability to absorb water are evaluated, and combined with the dynamic changes of soil moisture, the root water use efficiency index is obtained.

4. The ground-to-air integrated agricultural detection system according to claim 3, characterized in that: The specific process of identifying crop disease symptoms and pest population distribution and predicting the spread trend of pests and diseases is as follows: Based on farmland remote sensing image data and disease sensor data, identify the symptoms of crop diseases, capture the distribution of pest populations in farmland, and conduct cross-validation with ground data; Through statistical analysis, the key characteristics of pest and disease transmission are extracted, a pest and disease spread prediction model is constructed, the impact of environmental factors on pest and disease transmission is analyzed, and a pest and disease spread trend map is generated.

5. The ground-to-air integrated agricultural detection system according to claim 4, characterized in that: The specific process of obtaining the pest transmission risk index is as follows: Based on the identified pest and disease symptoms and pest population distribution, risk factor analysis is conducted in combination with environmental factors to determine the key variables that affect the spread of pests and diseases; Based on the pest and disease spread prediction model and combined with the key variables of pest and disease transmission, the pest and disease transmission risk index is obtained.

6. The ground-to-air integrated agricultural detection system according to claim 5, characterized in that: The specific process of comprehensively analyzing soil particle loss, surface runoff intensity and vegetation cover changes to obtain the soil erosion risk index is as follows: Based on environmental monitoring sensor data, which includes soil particle loss sensor data, surface runoff monitoring data and vegetation cover sensor data, the rate and pattern of soil particle loss are analyzed, and the potential impact of soil erosion is evaluated in combination with surface runoff intensity and vegetation cover changes to obtain a soil erosion risk index.

7. The ground-to-air integrated agricultural detection system according to claim 6, characterized in that: The specific process of obtaining the farmland detection index is as follows: The root water use efficiency index, pest and disease transmission risk index and soil erosion risk index were assigned weights through the hierarchical analysis method, and a comprehensive analysis was performed to obtain the farmland detection index.

8. A ground-air integrated agricultural detection method, applied to a ground-air integrated agricultural detection system as claimed in any one of claims 1 to 7, characterized in that: The following steps are involved: S1. Obtain multi-dimensional data of farmland through ground and air sensors; S2. Based on multi-dimensional farmland data, analyze the dynamic changes of soil moisture and crop root activity to obtain the root water use efficiency index; S3. Based on multi-dimensional farmland data, identify crop disease symptoms and pest population distribution, predict the spread trend of pests and diseases, and obtain the pest and disease transmission risk index; S4. Based on the multi-dimensional data of farmland, a comprehensive analysis of soil particle loss, surface runoff intensity and vegetation cover changes is conducted to obtain the soil erosion risk index; S5. Comprehensively evaluate the root water use efficiency index, pest and disease transmission risk index and soil erosion risk index, obtain the farmland detection index, set up an early warning mechanism, and generate early warning information when the farmland detection index triggers the early warning mechanism.

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