Intelligent forest grass planting and reseeding method

Abstract

The invention relates to the technical field of agriculture, and discloses an intelligent forest and grass planting and reseeding method which comprises the following steps: S1, collecting multi-modal data of vegetation, soil and weather through remote sensing of an unmanned aerial vehicle and a ground sensor, and generating a comprehensive monitoring report by using a multi-modal data fusion algorithm; s2, analyzing a vegetation health state based on the fused data, delimiting a reseeding priority region, and generating a reseeding map and path planning; s3, performing reseeding on the target area according to the planned path by adopting modular intelligent seeding equipment, and dynamically adjusting seeding parameters; and S4, regularly monitoring the reseeding area, generating a reseeding effect evaluation report, and optimizing a secondary reseeding scheme according to an evaluation result. According to the invention, multi-dimensional data such as vegetation coverage rate, health index, soil humidity, temperature, nutrient content and the like are comprehensively acquired through the multi-modal data acquisition module by using a multi-spectral camera carried by the unmanned aerial vehicle, a thermal infrared sensor and a ground sensor network.

Description

Technical Field

[0001] The present invention relates to the field of agricultural technology, and in particular to an intelligent forest and grass planting and reseeding method. Background Art

[0002] Globally, with the deterioration of the ecological environment, the expansion of desertification and the intensification of climate change, the decline in vegetation coverage has become an important issue restricting the sustainable development of the ecological environment. Especially in arid and semi-arid areas, the degradation of forests and grasslands has led to soil erosion, reduction of biodiversity and weakening of climate regulation functions, which has had a significant impact on agricultural production, ecological protection and social and economic development.

[0003] In traditional reseeding operations, the basis for determining the reseeding area and timing is often obtained through manual visual inspection or accumulated experience. Reseeding operators usually observe the vegetation coverage on site and roughly judge the reseeding needs based on the dryness and wetness of the soil surface. However, the traditional reseeding process does not collect and utilize key data such as soil moisture, temperature, and nutrients. The operation mostly relies on experience and judgment, which lacks scientificity. Summary of the invention

[0004] In order to make up for the above shortcomings, the present invention provides an intelligent forest and grass planting overseeding method, which aims to improve the insufficient collection and utilization of key data such as soil moisture, temperature, nutrients, etc. in the traditional overseeding process, and the operation mostly relies on experience judgment and lacks scientificity.

[0005] In a first aspect, the present invention provides the following technical solution, a method for intelligent forest and grass planting and reseeding, comprising the following steps:

[0006] S1. Multimodal data collection: Collect multimodal data of vegetation, soil and meteorology through UAV remote sensing and ground sensors, and use multimodal data fusion algorithms to generate comprehensive monitoring reports;

[0007] S2. Regional diagnosis and planning: Analyze the health status of vegetation based on fusion data, define the priority areas for reseeding, and generate reseeding maps and path planning;

[0008] S3. Intelligent seeding implementation: Modular intelligent seeding equipment is used to implement reseeding in the target area according to the planned path and dynamically adjust the seeding parameters;

[0009] S4. Post-reseeding monitoring and feedback: Regularly monitor the reseeding area, generate a reseeding effect evaluation report, and optimize the secondary reseeding plan based on the evaluation results.

[0010] Preferably, the multimodal data collection includes:

[0011] S101. Use drones with multispectral cameras and thermal infrared sensors to obtain vegetation coverage, NDVI and terrain elevation data;

[0012] S102. Deploy soil moisture, temperature and nutrient sensors to collect soil moisture content, nitrogen, phosphorus and potassium content and temperature in real time;

[0013] S103. Use multimodal data fusion algorithms to combine UAV remote sensing data with ground sensor data to generate vegetation health maps, soil nutrient maps, and water distribution maps.

[0014] Preferably, the regional diagnosis and planning includes:

[0015] S201. Analyze the NDVI value of vegetation coverage based on the fused data and divide the area into healthy area, sparse area and bare area;

[0016] S202. Evaluate the reseeding needs of sparse or bare land areas by combining soil moisture, nutrient and meteorological data using a multimodal data fusion algorithm;

[0017] S203. Use a deep reinforcement learning algorithm to predict the appropriate reseeding time window and generate a sowing path plan based on an optimization algorithm;

[0018] S204. Use the Geographic Information System (GIS) to generate reseeding priority zones and path planning.

[0019] Preferably, the intelligent seeding implementation includes:

[0020] S301. Use modular intelligent seeding equipment, use shallow tillage seeding mode in flat or low-slope areas, and use drone precision delivery mode in high-slope or complex terrain areas;

[0021] S302. Dynamically adjust the sowing depth and amount according to real-time soil moisture data to make the seeds evenly distributed;

[0022] S303. The sowing equipment has a built-in seed coating module to add drought-resistant agents or nutrient coatings according to soil characteristics.

[0023] Preferably, the multimodal data fusion algorithm uses Kalman filtering or a deep learning fusion model to integrate vegetation images, soil data and meteorological parameters, eliminate outliers and generate a monitoring report.

[0024] Preferably, the post-broadcast monitoring and feedback includes:

[0025] S401. Use UAV remote sensing and ground sensors to regularly monitor the reseeding area to obtain vegetation cover changes and soil environment dynamics;

[0026] S402, automatically generate a reseeding effect evaluation report, including vegetation coverage improvement rate, seed germination rate and seedling rate.

[0027] Preferably, after the step S4, when the reseeding effect does not meet expectations, a secondary reseeding plan is generated to adjust the seeding depth, the amount of seeding and the path planning.

[0028] In a second aspect, the present invention provides the following technical solution, an intelligent forest and grass planting and reseeding system, comprising:

[0029] Multimodal data acquisition module, which is used to collect multimodal data of vegetation, soil and meteorology through UAV remote sensing and ground sensors, and generate comprehensive monitoring reports using multimodal data fusion algorithms;

[0030] The regional diagnosis and planning module is used to analyze the health status of vegetation based on fused data, delineate priority areas for reseeding, and generate reseeding maps and path planning;

[0031] Intelligent seeding module, which uses modular intelligent seeding equipment to implement reseeding in the target area according to the planned path and dynamically adjust the seeding parameters;

[0032] The monitoring and feedback module is used to regularly monitor the reseeding area, generate a reseeding effect evaluation report, and optimize the secondary reseeding plan based on the evaluation results.

[0033] In the third aspect, the invention provides the following technical solution: a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned intelligent forest and grass planting reseeding method when executing the computer program.

[0034] In a fourth aspect, the present invention provides the following technical solution: a readable storage medium having a computer program stored thereon, and the computer program, when executed by a processor, implements the above-mentioned intelligent forest and grass planting and reseeding method.

[0035] The present invention has the following beneficial effects:

[0036] 1. In the present invention, through the multimodal data acquisition module, the multispectral camera, thermal infrared sensor and ground sensor network carried by the drone are used to comprehensively obtain multi-dimensional data such as vegetation coverage, health index, soil moisture, temperature, nutrient content, etc. Combined with the multimodal data fusion algorithm, the drone image data and ground sensor data are deeply analyzed to generate high-precision vegetation health status map and soil property distribution map. This precise regional diagnosis method can clearly define the priority areas for reseeding, identify sparse vegetation or bare land areas, reduce the waste of resources caused by blind sowing, and thus achieve accurate reseeding.

[0037] 2. In the present invention, historical meteorological data (such as rainfall, temperature) and real-time soil environment data are simulated and predicted through a deep reinforcement learning algorithm, the effects of different reseeding times on seed germination and vegetation recovery are analyzed, and the optimal reseeding time window is scientifically output. For example, reseeding 24 to 48 hours before the predicted rainfall can maximize the use of natural rainfall to increase the seed germination rate and avoid reseeding failures due to drought or insufficient soil moisture. This time window optimization function significantly improves the success rate of planting and seed utilization efficiency.

[0038] 3. In the present invention, the intelligent sowing module is modularly designed, combining shallow tillage sowing and drone precision delivery modes to adapt to different terrain conditions. In flat or low-slope areas, the equipment adopts shallow tillage mode to loosen the soil appropriately before precise sowing to ensure close contact between seeds and soil. In high slopes or complex terrains, drones use precision delivery mode to evenly distribute seeds in the target area without the need for complex ground construction operations. This combined sowing method greatly expands the scope of application of reseeding technology, and is particularly suitable for areas that are difficult for traditional machinery to reach, such as mountainous areas and desertified land. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A method flow chart of an intelligent forest and grass planting and reseeding method proposed by the present invention;

[0040] Figure 2 This is a system architecture diagram of an intelligent forest and grass planting and reseeding system proposed in the present invention. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present invention will be described clearly and completely below 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 creative work are within the scope of protection of the present invention.

[0042] Embodiment 1

[0043] Reference Figure 1 In a first embodiment of the present invention, the present invention provides an intelligent forest and grass planting and reseeding method, comprising the following steps:

[0044] S1. Multimodal data collection: Collect multimodal data of vegetation, soil and meteorology through UAV remote sensing and ground sensors, and use multimodal data fusion algorithms to generate comprehensive monitoring reports;

[0045] S2. Regional diagnosis and planning: Analyze the health status of vegetation based on fusion data, define the priority areas for reseeding, and generate reseeding maps and path planning;

[0046] S3. Intelligent seeding implementation: Modular intelligent seeding equipment is used to implement reseeding in the target area according to the planned path and dynamically adjust the seeding parameters;

[0047] S4. Post-reseeding monitoring and feedback: Regularly monitor the reseeding area, generate a reseeding effect evaluation report, and optimize the secondary reseeding plan based on the evaluation results.

[0048] Specifically, this method significantly improves the accuracy, efficiency and adaptability of reseeding. Through the multimodal data acquisition module, drone remote sensing and ground sensors are used to collect high-precision data on vegetation, soil and meteorology, and the vegetation health status map and soil property distribution map are generated in combination with the multimodal data fusion algorithm to accurately define the priority areas for reseeding and reduce the waste of resources caused by blind sowing. At the same time, the system uses deep reinforcement learning algorithms to analyze historical meteorological and soil data, scientifically predict the best reseeding time window (such as 24-48 hours before rainfall), and effectively improve the seed germination rate and vegetation recovery effect. The modular intelligent sowing equipment supports two modes: shallow tillage sowing and drone precision delivery. It combines GIS path planning and ant colony algorithm to optimize the sowing path, adapt to different terrain conditions, achieve efficient operations in complex terrains, and significantly improve sowing efficiency.

[0049] Multimodal data collection includes:

[0050] S101. Use drones with multispectral cameras and thermal infrared sensors to obtain vegetation coverage, NDVI and terrain elevation data;

[0051] S102. Deploy soil moisture, temperature and nutrient sensors to collect soil moisture content, nitrogen, phosphorus and potassium content and temperature in real time;

[0052] S103. Use multimodal data fusion algorithms to combine UAV remote sensing data with ground sensor data to generate vegetation health maps, soil nutrient maps, and water distribution maps.

[0053] Specifically, S101, UAV remote sensing acquisition

[0054] a) Collect vegetation coverage, vegetation health index (NDVI) and terrain elevation data using drones equipped with multispectral cameras and thermal infrared sensors;

[0055] b) LiDAR generates digital elevation models (DEMs) to support terrain analysis;

[0056] c) The flight altitude of the drone is set at 100 to 150 meters, and the speed is controlled at 5-10m / s to ensure coverage and data accuracy.

[0057] S102, ground sensor collection

[0058] a) Deploy soil moisture sensors (0% to 100%), soil temperature sensors (-10°C to 50°C) and nutrient sensors to monitor nitrogen, phosphorus and potassium concentrations in real time;

[0059] b) A set of sensors is arranged in every 50m×50m grid to vertically monitor different depths (e.g. 0-10cm, 10-30cm).

[0060] S103, Multimodal Data Fusion

[0061] a) Use Kalman filtering algorithm to remove data outliers and ensure time and space alignment;

[0062] b) Use deep learning fusion models to integrate UAV remote sensing data with ground sensor data to generate vegetation health maps, soil nutrient maps, and water distribution maps.

[0063] Regional diagnosis and planning include:

[0064] S201. Analyze the NDVI value of vegetation coverage based on the fused data and divide the area into healthy area, sparse area and bare area;

[0065] S202. Evaluate the reseeding needs of sparse or bare land areas by combining soil moisture, nutrient and meteorological data using a multimodal data fusion algorithm;

[0066] S203. Use a deep reinforcement learning algorithm to predict the appropriate reseeding time window and generate a sowing path plan based on an optimization algorithm;

[0067] S204. Use the Geographic Information System (GIS) to generate reseeding priority zones and path planning.

[0068] Specifically, S201, health status analysis

[0069] a) Based on the NDVI value, the area was divided into healthy area (NDVI>0.7), sparse area (0.3≤NDVI≤0.7) and bare area (NDVI<0.3);

[0070] b) Combined with thermal infrared data, further assessment of vegetation drought stress status.

[0071] S202. Assessment of reseeding needs

[0072] a) Integrate soil moisture, nutrient and meteorological data to determine the reseeding intensity requirements for sparse and bare areas;

[0073] b) Recommend appropriate seed types and sowing rates.

[0074] S203. Time window prediction

[0075] a) Use deep reinforcement learning algorithms combined with historical meteorological data to predict the best time window for reseeding;

[0076] b) Simulate the effects of different sowing times and output recommended times, such as 24-48 hours before rainfall.

[0077] S204, Path Planning

[0078] a) Generate reseeding priority zones using Geographic Information System (GIS) technology;

[0079] b) Optimize the seeding path through the ant colony algorithm to ensure maximum coverage and minimize equipment operating time.

[0080] Smart Seeding implementation includes:

[0081] S301. Use modular intelligent seeding equipment, use shallow tillage seeding mode in flat or low-slope areas, and use drone precision delivery mode in high-slope or complex terrain areas;

[0082] S302. Dynamically adjust the sowing depth and amount according to real-time soil moisture data to make the seeds evenly distributed;

[0083] S303. The sowing equipment has a built-in seed coating module to add drought-resistant agents or nutrient coatings according to soil characteristics.

[0084] The multimodal data fusion algorithm uses Kalman filtering or deep learning fusion models to integrate vegetation images, soil data and meteorological parameters, eliminate outliers and generate monitoring reports.

[0085] Specifically, S301, path execution and mode switching

[0086] a) In flat or low-slope areas, the sowing equipment adopts shallow tillage mode and the tillage depth is set to 1-3cm;

[0087] b) On high slopes or in complex terrain areas, drones can accurately drop seeds, with a drop frequency set to 5-10 seeds per second, and adjust the flight altitude and drop angle in real time.

[0088] S302, Ecological Parameter Adjustment

[0089] a) Dynamically adjust the sowing depth (1-5cm) based on real-time soil moisture data;

[0090] b) Reduce the amount of placement to 20-30 particles / m in wet areas 2 In arid areas, the number should be increased to 40-50 particles / m 2 .

[0091] S303, seed treatment

[0092] a) The seeding equipment has a built-in seed coating module, which dynamically adjusts the coating ingredients according to soil characteristics and adds drought-resistant agents and nutrient coatings;

[0093] b) After the seeds are processed, they are evenly distributed through a rotating spreading mechanism.

[0094] Post-reseeding monitoring and feedback include:

[0095] S401. Use UAV remote sensing and ground sensors to regularly monitor the reseeding area to obtain vegetation cover changes and soil environment dynamics;

[0096] S402, automatically generate a reseeding effect evaluation report, including vegetation coverage improvement rate, seed germination rate and seedling rate.

[0097] Specifically, S401, vegetation monitoring

[0098] a) Drones regularly scan the reseeding area, collect data on vegetation coverage changes, and generate vegetation recovery curves;

[0099] b) Ground sensors upload soil moisture and nutrient dynamics in real time to monitor the suitability of the growing environment.

[0100] S402, Effect Evaluation

[0101] a) Automatically generate an evaluation report on the effect of reseeding, including the improvement of vegetation coverage, seed germination rate and seedling rate;

[0102] b) If the reseeding effect does not meet the requirements, analyze the influencing factors, such as soil conditions or meteorological changes.

[0103] After step S4, if the reseeding effect does not meet expectations, a secondary reseeding plan is generated to adjust the seeding depth, delivery amount and path planning.

[0104] Specifically, a) based on the evaluation results, re-delineate the reseeding area and optimize the sowing parameters, including depth, amount and path;

[0105] b) The system generates a secondary reseeding plan and executes it through modular equipment.

[0106] Embodiment 2:

[0107] Reference Figure 2 In a second embodiment of the present invention, the present invention provides an intelligent forest and grass planting and reseeding system, comprising:

[0108] Multimodal data acquisition module, which is used to collect multimodal data of vegetation, soil and meteorology through UAV remote sensing and ground sensors, and generate comprehensive monitoring reports using multimodal data fusion algorithms;

[0109] The regional diagnosis and planning module is used to analyze the health status of vegetation based on fused data, delineate priority areas for reseeding, and generate reseeding maps and path planning;

[0110] Intelligent seeding module, which uses modular intelligent seeding equipment to implement reseeding in the target area according to the planned path and dynamically adjust the seeding parameters;

[0111] The monitoring and feedback module is used to regularly monitor the reseeding area, generate a reseeding effect evaluation report, and optimize the secondary reseeding plan based on the evaluation results.

[0112] Specifically, 1. System overall architecture

[0113] The intelligent forest and grass planting reseeding system aims to achieve accurate reseeding operations in complex environments. Through the four functional modules of multimodal data collection, regional diagnosis and planning, intelligent seeding, and monitoring and feedback, a closed-loop process of "perception-analysis-decision-making-execution-feedback" is constructed. The system includes the following main components:

[0114] Hardware Composition

[0115] UAV system: Equipped with a multispectral camera, thermal infrared sensor and lidar to collect vegetation and terrain data.

[0116] Ground sensor network: including soil moisture, temperature, and nutrient sensors for real-time monitoring of environmental data.

[0117] Modular intelligent seeding equipment: supports multiple seeding modes, adapts to different terrain conditions, and achieves precise seeding.

[0118] Cloud server: responsible for data fusion, analysis and decision support.

[0119] Software Support

[0120] Data processing module: includes image processing algorithm, spatiotemporal alignment module and multimodal data fusion algorithm.

[0121] Intelligent planning module: Combines deep reinforcement learning algorithms and GIS spatial analysis tools to generate reseeding paths and priority zones.

[0122] Real-time control module: dynamically adjusts seeding parameters based on real-time monitoring data.

[0123] 2. Multimodal data acquisition module

[0124] Function description: Collect multi-source data such as vegetation, soil and meteorology to provide a basis for subsequent analysis.

[0125] UAV remote sensing submodule

[0126] Configuration: The drone is equipped with a multispectral camera (covering the 400-1000nm band), a thermal infrared sensor and a laser radar (Li DAR), and is equipped with an RTKGPS positioning system.

[0127] Workflow:

[0128] Set the route and flight altitude (100-150 meters), the flight speed 5-10m / s, and collect multispectral images, thermal infrared data and terrain point cloud data in the area.

[0129] The data is transmitted to the ground station in real time and stored in the server through the wireless communication module.

[0130] Data preprocessing: The orthophoto stitching algorithm is used to eliminate the overlapping errors of aerial photos, and the radiation correction algorithm is combined to optimize the image quality.

[0131] Ground sensor submodule

[0132] Configuration: Soil moisture, temperature, and nutrient sensors are deployed, and LoRa or NB-I oT communication technology is used to transmit data. The power supply is solar energy and low-power batteries.

[0133] Workflow:

[0134] Sensors are deployed in a grid pattern (50m×50m grid) to achieve uniform data coverage in the area.

[0135] The data collection frequency is from 10 minutes to 1 hour, and soil moisture (0% to 100%), temperature (-10°C to 50°C) and nutrient concentration are obtained.

[0136] Data fusion submodule

[0137] The Kalman filter algorithm is used to align the time and space of the data collected by the UAV and ground sensors and remove outliers.

[0138] A deep learning fusion model (convolutional neural network + recurrent neural network) is applied to integrate spatial and temporal dimension data to generate vegetation health maps, soil nutrient maps, and water distribution maps.

[0139] 3. Regional diagnosis and planning module

[0140] Functional description: Analyze the collected data, define the priority areas for reseeding, predict the best time window and plan the sowing path.

[0141] Health status analysis submodule

[0142] The NDVI values ​​were used to divide the area into healthy area (NDVI>0.7), sparse area (0.3≤NDVI≤0.7) and bare area (NDVI<0.3).

[0143] Thermal infrared images are combined with NDVI values ​​to identify drought stress areas and generate vegetation health zoning maps.

[0144] Reseeding demand assessment submodule

[0145] Comprehensively consider soil moisture, nutrients and meteorological conditions, analyze the need for overseeding in sparse and bare areas, and determine the appropriate seed type and amount to be released.

[0146] Time window prediction submodule

[0147] The model is trained using a deep reinforcement learning algorithm and combined with historical meteorological data to simulate the impact of different sowing times on the reseeding effect, and to predict the optimal reseeding time window (such as 24-48 hours before rainfall).

[0148] Path planning submodule

[0149] The seeding path is generated based on GIS tools, and the path length and coverage are optimized by combining the ant colony algorithm.

[0150] 4. Intelligent seeding module

[0151] Function description: Implement seeding according to the planned path and dynamically adjust seeding parameters to improve seeding efficiency and effect.

[0152] Equipment control submodule

[0153] The seeding equipment uses shallow tillage mode (tillage depth 1-3cm) on flat ground and drone precision delivery mode (delivery frequency 5-10 seeds / second) on high slopes or complex terrain.

[0154] Dynamically adjust submodules

[0155] Dynamically adjust the sowing depth (1-5 cm) and the amount of sowing (20-50 seeds / m) according to the real-time soil moisture data. 2 ), increase the sowing depth in arid areas and reduce the amount of seed placed in wet areas.

[0156] Seed treatment submodule

[0157] Equipped with a seed coating module, it dynamically adjusts the coating materials (such as drought-resistant agents, nutrient coatings) according to soil characteristics to improve seed germination rate.

[0158] 5. Monitoring and feedback module

[0159] Function description: After reseeding is completed, monitor vegetation growth and environmental dynamics, generate effect evaluation reports and optimize secondary reseeding plans.

[0160] Vegetation monitoring submodule

[0161] Drones regularly scan the reseeding areas to generate NDVI change curves and coverage distribution maps.

[0162] Environmental monitoring submodule

[0163] Ground sensors continuously monitor soil moisture and nutrient dynamics to assess the seed growth environment.

[0164] Effect evaluation submodule

[0165] Automatically generate a reseeding effect report, including the improvement of vegetation coverage, seed germination rate and seedling rate.

[0166] Secondary reseeding optimization submodule

[0167] When the effect does not meet expectations, a secondary reseeding plan is regenerated, including optimization of seeding depth, release amount and path planning.

[0168] Embodiment 3

[0169] The third embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the intelligent forest and grass planting and reseeding method of the above embodiment.

[0170] Embodiment 4

[0171] The fourth embodiment of the present invention is based on the same inventive concept. A computer device proposed by the present invention comprises: a processor and a memory. The processor and the memory communicate with each other. The memory is used to store instructions. The processor is used to execute the instructions in the memory to execute the intelligent forest and grass planting and reseeding method of the above embodiment.

[0172] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0173] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent forest and grass planting and reseeding method, characterized in that: The following steps are involved: S1. Multimodal data collection: Collect multimodal data of vegetation, soil and meteorology through UAV remote sensing and ground sensors, and use multimodal data fusion algorithms to generate comprehensive monitoring reports; S2. Regional diagnosis and planning: Analyze the health status of vegetation based on fusion data, define the priority areas for reseeding, and generate reseeding maps and path planning; S3. Intelligent seeding implementation: Modular intelligent seeding equipment is used to implement reseeding in the target area according to the planned path and dynamically adjust the seeding parameters; S4. Post-reseeding monitoring and feedback: Regularly monitor the reseeding area, generate a reseeding effect evaluation report, and optimize the secondary reseeding plan based on the evaluation results.

2. The intelligent forest and grass planting and reseeding method according to claim 1 is characterized in that: The multimodal data collection includes: S101. Use drones with multispectral cameras and thermal infrared sensors to obtain vegetation coverage, NDVI and terrain elevation data; S102. Deploy soil moisture, temperature and nutrient sensors to collect soil moisture content, nitrogen, phosphorus and potassium content and temperature in real time; S103. Use multimodal data fusion algorithms to combine UAV remote sensing data with ground sensor data to generate vegetation health maps, soil nutrient maps, and water distribution maps.

3. The intelligent forest and grass planting and reseeding method according to claim 1 is characterized in that: The regional diagnosis and planning include: S201. Analyze the NDVI value of vegetation coverage based on the fused data and divide the area into healthy area, sparse area and bare area; S202. Evaluate the reseeding needs of sparse or bare land areas by combining soil moisture, nutrient and meteorological data using a multimodal data fusion algorithm; S203. Use a deep reinforcement learning algorithm to predict the appropriate reseeding time window and generate a sowing path plan based on an optimization algorithm; S204. Use the Geographic Information System (GIS) to generate reseeding priority zones and path planning.

4. The intelligent forest and grass planting and reseeding method according to claim 1 is characterized in that: The intelligent seeding implementation includes: S301. Use modular intelligent seeding equipment, use shallow tillage seeding mode in flat or low-slope areas, and use drone precision delivery mode in high-slope or complex terrain areas; S302. Dynamically adjust the sowing depth and amount according to real-time soil moisture data to make the seeds evenly distributed; S303. The sowing equipment has a built-in seed coating module to add drought-resistant agents or nutrient coatings according to soil characteristics.

5. The intelligent forest and grass planting and reseeding method according to claim 1 is characterized in that: The multimodal data fusion algorithm adopts Kalman filtering or deep learning fusion model to integrate vegetation images, soil data and meteorological parameters, eliminate outliers and generate monitoring reports.

6. The intelligent forest and grass planting and reseeding method according to claim 1 is characterized in that: The post-reseeding monitoring and feedback includes: S401. Use UAV remote sensing and ground sensors to regularly monitor the reseeding area to obtain vegetation cover changes and soil environment dynamics; S402, automatically generate a reseeding effect evaluation report, including vegetation coverage improvement rate, seed germination rate and seedling rate.

7. The intelligent forest and grass planting and reseeding method according to claim 1 is characterized in that: After the step S4, if the reseeding effect does not meet expectations, a secondary reseeding plan is generated to adjust the seeding depth, the amount of seeding and the path planning.

8. An intelligent forest and grass planting and reseeding system, characterized in that: The intelligent forest and grass planting and reseeding method according to any one of claims 1 to 7 comprises: Multimodal data acquisition module, which is used to collect multimodal data of vegetation, soil and meteorology through UAV remote sensing and ground sensors, and generate comprehensive monitoring reports using multimodal data fusion algorithms; The regional diagnosis and planning module is used to analyze the health status of vegetation based on fused data, delineate priority areas for reseeding, and generate reseeding maps and path planning; Intelligent seeding module, which uses modular intelligent seeding equipment to implement reseeding in the target area according to the planned path and dynamically adjust the seeding parameters; The monitoring and feedback module is used to regularly monitor the reseeding area, generate a reseeding effect evaluation report, and optimize the secondary reseeding plan based on the evaluation results.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the intelligent forest and grass planting and reseeding method as described in any one of claims 1 to 7 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the intelligent forest and grass planting and reseeding method as described in any one of claims 1 to 7 is implemented.

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