9438 results about "Agriculture" patented technology

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a high-standard farmland intelligent irrigation system based on Internet of Things and data analysis, and the system comprises a soil moisture content sensing module which collects data through a multi-source sensor to construct a three-dimensional soil moisture content distribution model, and generates a soil moisture content characteristic spectrum; the soil moisture content prediction module generates water demand prediction data based on the soil moisture content characteristic spectrum, the meteorological data and the crop growth stage; the irrigation strategy module fuses terrain elevation and pipe network pressure parameters to generate an irrigation control map; the equipment state monitoring module collects water pump current waveform and other data to generate equipment health degree parameters; the pipe network optimization module optimizes pipe network topology and generates an adjusting instruction; the multi-source data fusion module generates fusion evaluation indexes by using an evidence theory, an entropy weight method and the like; and the intelligent execution module generates an execution control instruction accordingly. All the modules cooperate to achieve precise irrigation, and the utilization efficiency of water resources and the intelligent level of farmland management are improved.

A method for generating an agricultural cut-point for an agricultural item includes capturing an image of the agricultural item, generating a depth estimation of the agricultural item, segmenting the image of the agricultural item to generate a segmented image that identifies different segments of the agricultural item, detecting an agricultural feature of the agricultural item based on the image of the agricultural item, generating a two-dimensional cut-point based on the segmented image and the agricultural feature, and generating a three-dimensional cut-point based on the two-dimensional cut-point and the depth estimation of the agricultural item.

The present application relates to a kind of deletion mutant nucleic acid and its application in anti-herbicide.The deletion mutation occurs in the promoter of rice OsHPPD Gene, the length of deletion sequence is at least 10 bp, and the A in the start codon ATG of gene is 0, at least the bases located in the upstream of start codon ATG from 2041 to 2032 are deleted. OsHPPD The present application finds that by deleting part of the sequence in the promoter of rice OsHPPD Gene, rice can obtain resistance to HPPD inhibitor herbicides, which is of great application value for ensuring agricultural production safety and improving the efficiency of herbicide use.

The invention relates to the field of agricultural data analysis, in particular to a japonica rice paddy field soil carbon nitrogen ratio dynamic intelligent assessment method, which comprises the following steps: acquiring and preprocessing japonica rice paddy field environment time sequence data to obtain a standardized environment data set; performing nonlinear congestion analysis on the soil moisture content state to obtain a corrected moisture dimension distance component; obtaining a corrected temperature dimension distance component by evaluating the metabolism permissible degree of the soil moisture on the temperature effect; performing double weighted fusion on the moisture and temperature correction distance components to obtain a soil environment state clustering result; through coupling calculation of a soil environment state clustering result and a first-order dynamic decomposition model, a dynamic intelligent evaluation result of the carbon-nitrogen ratio of the japonica rice paddy field soil is obtained, so that the problem that existing clustering cannot accurately distinguish aerobic and anaerobic environment states in a japonica rice paddy field scene and causes deviation of the evaluation result of the carbon-nitrogen ratio of the soil is solved.

The invention discloses a crop environment quantitative evaluation and decision making system based on growth stage self-adaption, and relates to the technical field of agricultural intelligent decision making. Acquiring an environment original data sequence of field multi-type sensors through a data acquisition module; the dynamic identification module analyzes the sequence by using a preset crop growth stage discrimination model, identifies the current growth stage and outputs a corresponding key environment parameter weight template; the feature fusion module performs weighted fusion on the original data according to the template to generate an environment feature vector with stage adaptability; the state evaluation module converts the vector into a growth state quantitative evaluation value through a growth state evaluation model; and the decision trigger judgment module calls a corresponding preset dynamic threshold interval according to the growth stage for comparison, and generates a decision trigger instruction when the growth state quantitative evaluation value deviates. According to the invention, stage self-adaptive intelligentization of crop growth monitoring and decision making is realized, and the accuracy of environment state evaluation and the timeliness of management decision making are improved.

A crop growth modeling system includes a memory configured to store computer-readable instructions. The instructions cause to the system to use a digital twin component configured to create and manage a digital twin of a farm. The instructions cause to the system to use a data input component configured to receive data related to a defined set of land characteristics and environmental attributes for the farm. The instructions cause to the system to use a processing component configured to integrate the received data with the digital twin and to simulate at least one crop growth scenario based on the integrated data. The instructions cause to the system to use a prediction component configured to determine a predicted crop growth rate for the farm based on the simulations conducted by the processing component.

The invention discloses an intelligent water and fertilizer integrated irrigation system based on an Internet of Things sensor and a large language model, and relates to the technical field of intelligent agriculture. The system obtains soil, weather and plant growth state data in real time through Internet of Things sensors, including a soil sensor, a weather sensor and a multispectral sensor, deployed in a farmland; the method comprises the following steps: based on a large language model architecture, integrating preset agricultural field knowledge base data, performing field adaptive fine tuning on model parameters by adopting a data migration technology, and constructing an intelligent irrigation control model with a dynamic decision-making function; the control model analyzes sensor data and crop growth requirements, generates a water and fertilizer supply strategy, calls an internet-of-things control interface of the water and fertilizer all-in-one machine, and automatically adjusts irrigation water quantity and proportional supply of nutrient elements such as nitrogen, phosphorus and potassium. According to the invention, Internet of Things perception and large model decision are fused, and different environments are adapted through fine adjustment, so that closed-loop precise control is realized, and the water and fertilizer utilization rate and the crop yield are remarkably improved. Actual measurement shows that the system can improve the water and fertilizer utilization rate by 30% or above, and the crop yield is increased by 15-20%.

The invention discloses a crop nitrogen fertilizer management system and method based on multi-agent reinforcement learning, and belongs to the technical field of intelligent agriculture. Comprising the following steps: collecting and preprocessing multi-source data of a target area, calibrating a crop growth-nitrogen cycle model based on the multi-source data, and constructing a dynamic simulation environment; constructing a nitrogen fertilizer application strategy model and a multi-target award function, and performing agent reinforcement learning training by adopting a centralized training-decentralized execution architecture; introducing a large language model, and updating a strategy network and / or a value network of each agent in a training process according to a reward adjustment signal and a decision constraint; when the index fluctuation ratio in the continuous evaluation period is smaller than a preset threshold value, it is judged that the nitrogen fertilizer application strategy model is converged, and an optimal nitrogen fertilizer application strategy model is obtained; and generating a nitrogen fertilizer application scheme based on the optimal nitrogen fertilizer application strategy model in combination with the real-time state data, and generating a natural language interpretation and risk assessment report based on a large language model.

This application provides a wind-resistant, stable power generation agricultural-solar hybrid system, including pillars, beams, photovoltaic supports, and photovoltaic panels erected in farmland. The photovoltaic panels are equipped with a buffer mechanism and an overload release device. The buffer mechanism effectively absorbs kinetic energy and reduces the swaying amplitude and speed of the photovoltaic panels through its elasticity, and automatically resets the photovoltaic panels after strong winds. The overload release device protects the photovoltaic panels from damage caused by strong winds. This application overcomes the technical defects of existing static wind-resistant systems, reduces system wind resistance, can withstand super typhoons, provide stable power generation and irrigation, and promote agricultural production.

The invention relates to the technical field of agricultural equipment, in particular to a hybrid tractor whole machine dynamic property evaluation method fusing multiple working condition parameters, which comprises the following steps: acquiring an environment working condition parameter set, an operation task parameter set and an energy flow dynamic parameter set in real time; a dynamic coupling relation mathematical model is constructed based on the parameter set, the whole machine dynamic property characteristic values of the steady state working point and the transient working point are calculated respectively, and a whole machine dynamic property evaluation index set is generated; inputting the evaluation index set into a hardware-in-the-loop simulation platform, and verifying the effectiveness of the evaluation indexes through multi-modal working condition simulation; and constructing a three-dimensional dynamic property evaluation map based on the verified evaluation indexes, dividing dynamic property partitions, outputting the dynamic property partitions to the vehicle-mounted intelligent terminal in real time, and generating a self-adaptive control instruction set when entering a failure area. According to the invention, multi-source information fusion modeling, all-working-condition dynamic response evaluation and visual map feedback can be realized, and the dynamic adaptability and regulation and control intelligence of the hybrid tractor in a complex operation scene are improved.

The invention belongs to the technical field of agricultural microorganisms, and particularly relates to pseudomonas fluorescens as well as a microbial agent and application thereof. The invention discloses a pseudomonas fluorescens TJ194, which is preserved in the China General Microbiological Culture Collection Center (CGMCC), and the preservation number is CGMCC No.33697. The pseudomonas fluorescens TJ194 has the advantages that the pseudomonas fluorescens TJ194 can be used for preparing the pseudomonas fluorescens TJ194, and the pseudomonas fluorescens TJ194 can be used for preparing the pseudomonas fluorescens TJ194; tests prove that the strain has the activity of inhibiting the growth of fusarium pseudograminearum; by applying a fungicide containing the strain, the disease index of the wheat stem rot is remarkably reduced, and meanwhile, the fresh weight of the overground part and the underground part of wheat is increased, so that the pseudomonas fluorescens TJ194 and the fungicide thereof can be applied to prevention and treatment of the plant stem rot and promotion of plant growth.

The invention relates to the technical field of agricultural disease and pest prevention and control, and discloses a big-data-driven disease and pest green prevention and control decision-making method. The method comprises the following steps: collecting multi-source heterogeneous agricultural environment data containing meteorological elements, crop physiological indexes and field biological activity information, and generating a dynamic weight coefficient according to a data source temporal-spatial resolution and a measurement dimension; constructing a disease and pest risk dynamic evaluation model, and calculating disease and pest occurrence probability indexes in different growth stages in different regions based on coefficient fusion data; establishing a green prevention and control measure knowledge base, and matching a candidate prevention and control scheme set according to crop types and growth cycles; in combination with the disease and pest occurrence probability index and the candidate scheme set, the prevention and control effect, the environmental influence and the economic cost are weighed, and an optimal prevention and control strategy sequence is generated; and implementing a dynamic adjustment mechanism, updating a disease and pest occurrence probability index according to real-time farmland feedback data, iteratively correcting an optimal prevention and control strategy sequence, and assisting agricultural green sustainable production.

The invention discloses a winter wheat LAI and SPAD estimation method based on a lightweight semi-supervised model, and relates to the technical field of agricultural remote sensing monitoring, and the method comprises the steps: obtaining multispectral image data of a winter wheat key growth period, and carrying out the preprocessing of the multispectral image data to generate a standardized multichannel vegetation index image; the method comprises the following steps: constructing a lightweight semi-supervised model MCVI-SANet, and carrying out self-supervised training on the MCVI-SANet by adopting a semi-supervised training strategy driven by VICReg; and inputting the multi-channel vegetation index image into the trained MCVI-SANet, and outputting quantitative estimation results of the LAI and SPAD of the winter wheat. Through combination of a saturation perception mechanism and semi-supervised learning, estimation deviation caused by dense canopy vegetation index saturation and data noise is effectively eliminated, and the estimation precision and generalization ability in a complex agricultural scene are improved while the lightweight deployment characteristic of the model is ensured.

The invention provides a whole-crop explainable disease and pest diagnosis method and system based on a multi-mode large model. The method comprises the following steps: constructing a knowledge extraction model of a two-way cross attention mechanism based on relation guidance, extracting structured knowledge from authoritative agricultural data, and constructing a pest and disease knowledge enhancement database to dynamically retrieve prior knowledge of a target crop; a hierarchical image processing strategy is adopted, and global, local and target area multi-level feature information is extracted from an input image; inputting and priori knowledge are integrated into a comprehensive diagnosis instruction, and a thinking chain guiding module is introduced to guide a large model to carry out multi-step reasoning according to a reasoning path; and performing unified reasoning by using the multi-modal large model, and outputting a disease and pest diagnosis result and a diagnosis basis thereof. The method does not need manual marking of multi-modal data or retraining, can realize efficient diagnosis of whole crop diseases and insect pests under the condition that the multi-modal data and computing resources are limited, has low cost, strong generalization ability and high interpretability, and is suitable for large-scale agricultural production practice.

The invention relates to the technical field of intelligent agriculture, and particularly discloses an orchard intelligent water and fertilizer integration-based edge end fruit tree fertilizer demand prediction method, which comprises the following steps of: constructing each fruit tree as a graph node, and fusing canopy spatial characteristics extracted by unmanned aerial vehicle multispectral remote sensing and nutrient time sequence characteristics monitored by a soil sensor; space influence weights among nodes are calculated according to the terrain, the soil texture and the space distance, and a fruit tree individual relation graph is established; then, through a space-time diagram convolutional network, aggregating space neighborhood features and capturing time dynamic changes, and outputting target state features subjected to multi-scale enhancement; when the canopy conflicts with the soil characteristic indication, combined diagnosis is carried out based on a neighborhood consistency index and an adjacent node state, systematic nutrient stress and local non-nutrient factors are distinguished, and final fertilizer demand prediction is generated after the conflict is eliminated; a digital fertilization prescription map is generated through spatial interpolation, soil characteristics and slope correction, and the water and fertilizer integrated equipment is driven to execute precise operation.

The invention discloses an intelligent agricultural planting management method and system, and belongs to the technical field of intelligent agriculture, and the method comprises the steps of data layer construction, water and fertilizer prediction layer construction, planting optimization layer construction and agricultural planting management. The space-time adaptive water and fertilizer prediction layer construction method combining the crop physiological load and the crop growth stage is adopted, the water demand and nitrogen, phosphorus and potassium nutrient demands of crops in the future stage are accurately predicted, the crop growth state and environment changes are dynamically responded, the water and fertilizer demand difference in different growth stages is considered, and the water and fertilizer prediction efficiency is improved. The accuracy and applicability of prediction are improved through time-space fusion, and accurate fertilization can be realized; a planting optimization layer construction method combining digital twinborn and multi-objective optimization is adopted, a water and fertilizer management strategy is subjected to refined and dynamic optimization under the condition of considering a crop growth stage, a soil nutrient condition and a future weather condition, and an optimal management strategy capable of improving the yield, saving water and fertilizer resources and reducing the nitrogen leaching risk is generated.

The invention belongs to the technical field of agriculture, and particularly provides a compound microbial agent and application thereof. The compound microbial agent is prepared from bacillus velezensis RKN1111, bacillus subtilis N-3 and paenibacillus mucilaginosus HX-02, and the compound microbial agent is prepared from bacillus velezensis RKN1111, bacillus subtilis N-3 and paenibacillus mucilaginosus HX-02, and the compound microbial agent is prepared from bacillus velezensis RKN1111, bacillus subtilis N-3 and paenibacillus mucilaginosus HX-02. The compound microbial agent can effectively improve the pH and EC of saline alkali soil, increase the content of alkali-hydrolyzable nitrogen, available phosphorus and quick-acting potassium in the soil, and play a significant role in increasing the plant height, stem diameter, chlorophyll content and aboveground biomass of corn plants in coastal saline-alkali soil.

The invention discloses a juicy peach yield prediction method based on comprehensive data analysis, and particularly relates to the technical field of agricultural intelligent perception. The method comprises the following steps: collecting multi-source data of an orchard, and constructing a data set containing meteorological, physiological, remote sensing and soil information; fruit tree physiological response parameters are extracted, and a bimodal diagram structure fusing the spatial adjacency relation and the physiological state similarity is established in combination with the dynamic climate anomaly index; inputting the graph structure into a graph neural network model, extracting spatial-temporal characteristics, dynamically adjusting an edge weight and a propagation layer number, and constructing an adaptive model; performing region division and weighted summarization according to a model output result, and finally obtaining a predicted value of the total yield of the orchard; the method improves the prediction accuracy under the conditions of complex climate and unstable data, and is suitable for refined orchard management.

The invention relates to an intelligent monitoring system for prevention and control of diseases and insect pests for bean planting, in particular to the field of intelligent monitoring of prevention and control of agricultural diseases and insect pests, and can realize extremely early precise recognition and early warning of diseases and insect pests of bean crops. The weak lesion spectral features and the continuous deterioration trend of the plant can be sensitively captured in a stage which is difficult to perceive by naked eyes, real diseases and environmental interference are effectively distinguished, the system can automatically locate a suspicious lesion area by using a weak supervised learning mechanism and only needing image-level labeling, the dependence on expensive pixel-level labeling data is greatly reduced, and the accuracy of the system is improved. Finally, a high-confidence early warning signal is output through multi-source information collaborative decision, a scientific basis is provided for accurate and green prevention and control, blind pesticide application is effectively avoided, and crop health and yield are guaranteed.

The invention discloses a multi-drive process multi-factor agricultural non-point source pollution prediction method based on coupling meteorological numerical forecasting. The method comprises the following steps: firstly, introducing numerical weather forecast data, and constructing a high-precision weather driving field with kilometer-level space and hour-level time resolution by combining WRF dynamic downscaling, DEM terrain correction and conservation resampling; secondly, establishing a multi-drive process coupling model system which comprises a meteorological drive layer, a hydrological response layer, a pollutant migration layer and a crop feedback layer and is used for simulating runoff production, sediment erosion, nitrogen and phosphorus migration and transformation and crop transpiration and root nutrient absorption processes; thirdly, performing precision verification on a simulation result by using observation data, and identifying key meteorological and hydrological factors through an error transfer matrix and a sensitivity analysis method; and finally, realizing parameter adaptive correction by adopting a long short-term memory network, finishing parameter optimization in combination with a multi-target genetic algorithm, packaging the model chain through a containerization technology, and realizing cross-platform deployment and visual output of a pollution load result. The method can be used for agricultural non-point source pollution prediction and management.

The invention discloses an agricultural scene three-dimensional reconstruction method based on multi-modal sensor fusion, and belongs to the technical field of three-dimensional modeling. The method comprises the following steps: acquiring multi-modal data of a target scene based on a tracked robot, wherein the multi-modal data comprises image data acquired by a binocular camera, point cloud data acquired by a laser radar, time sequence inertial data acquired by an inertial measurement unit and position data acquired by a Beidou positioning system; extracting different modal data features in the multi-modal data, and carrying out heterogeneous fusion on the different modal data features to obtain multi-modal data fusion features; and agricultural scene three-dimensional reconstruction is carried out by using a three-dimensional dynamic construction method based on space-time-semantic joint embedding. According to the invention, the environment scene modeling precision of the agricultural robot under the interference of muddy, raised dust and vibration can be improved.

The invention discloses a compound microbial agent for reducing the cadmium content of brown rice and application of the compound microbial agent. Belongs to the technical field of agricultural microorganisms. The compound microbial agent is prepared from herbaceous spirillum, stenotrophomonas and coxakia, the herbaceous spirillum is herbaceous spirillum R3, and the preservation number of the herbaceous spirillum is CCTCC (China Center For Type Culture Collection) NO: M 20231757; the stenotrophomonas is stenotrophomonas R5, and the preservation number of the stenotrophomonas is CCTCC (China Center For Type Culture Collection) NO: M 20231758; the coxakia is coxakia P1-4, and the preservation number of the coxakia is CCTCC (China Center For Type Culture Collection) NO: M 20231759. Compared with the prior art, the compound microbial agent disclosed by the invention has the beneficial effects that the compound microbial agent disclosed by the invention can reduce the content of cadmium in brown rice, so that farmland soil polluted by light and medium cadmium can be repaired and produced at the same time, and the edible safety of agricultural product rice is ensured.

The invention relates to the technical field of intelligent fertilization, and discloses an agricultural precise fertilization method which comprises the following steps: step 1, synchronously collecting multi-modal real-time data of single wheat plants, and automatically identifying the current growth stage of the wheat plants; 2, searching historical fertility data of the position of the wheat; 3, fusing the real-time data, the historical fertility data and the growth stage label to generate an enhanced fusion feature vector; step 4, selecting a corresponding causal diagram model according to the current growth stage, and estimating an average processing effect of different fertilization schemes based on the enhanced fusion feature vector; and 5, inputting the enhanced fusion feature vector, the average processing effect estimated value and the growth stage label into the prediction model and the adjustment model. According to the system and the method, the fertilization accuracy and the crop response effect are improved, continuous optimization of the system is realized through a closed-loop intelligent decision framework, and a landing and evolvable efficient fertilization solution is provided for intelligent agriculture.

The invention discloses talaromyces purpurogenum as well as a fermented supernatant and application thereof. The preservation number of the talaromyces purpureogenus F-4 is CCTCC NO: M 20252023, and the talaromyces purpureogenus F-4 has the growth promoting effects of dissolving phosphorus, fixing nitrogen and generating siderophores, also has the disease preventing and treating effects of preventing and treating garlic root rot, potato scab and wheat stem rot, and also can resist salt stress, alkali stress, salt-alkali composite stress, heavy metal stress and osmotic pressure stress. The fermentation supernatant of the talaromyces purpureus F-4 can be used for inhibiting the growth of fusarium solani spores, streptomyces botrytis spores and fusarium pseudograminearum and the germination of the spores of the fusarium solani, the streptomyces botrytis and the fusarium pseudograminearum. The talaromyces purpurogenus and the fermented supernatant thereof can effectively control the growth and reproduction of plant pathogens, reduce the risk of disease occurrence, reduce the dependence on chemical pesticides, and reduce the risk of environmental pollution and agricultural product residues, and have important significance for promoting the sustainable development of agriculture.

The invention relates to an agricultural robot, and discloses an animal husbandry pushing robot autonomous navigation method based on a laser radar, which comprises a microsecond-level multi-sensor time synchronization architecture based on an FPGA (Field Programmable Gate Array); in a navigation layer, seamless switching between the GNSS and the laser SLAM is realized by constructing a virtual fusion observation value by utilizing extended Kalman filtering and a self-adaptive dynamic weight algorithm; in the sensing layer, an RGB-I multi-mode model fused with laser reflectivity and a biological micro-motion detection technology are adopted, and feed and biological obstacles are accurately distinguished; and in the execution layer, flexible self-adaptive material pushing is achieved in combination with material groove edge fitting and force-position hybrid control, and the navigation precision, the operation quality and the safety of the material pushing robot in the all-weather and unstructured pasture environment are effectively improved.

The invention provides a multi-modal knowledge graph construction method and system in the agricultural field, and relates to the technical field of data processing. The method comprises the following steps: performing recognition and structured expression on agricultural entities, relationships and events by fusing texts, images and time-space data to form an agricultural semantic representation model; calculating a confidence coefficient based on the evidence template and resolving conflicts, and generating a structured knowledge unit; an agricultural multi-modal knowledge graph is constructed on the basis of the event network, and dynamic updating is achieved through increment correction and self-correction; and finally, outputting an agricultural aid decision result with confidence by combining causal reasoning and confidence evaluation. The agricultural multi-modal knowledge graph construction method solves the problem that an agricultural multi-modal knowledge graph construction method in the prior art lacks a system modeling and closed-loop optimization mechanism for cross-modal alignment credibility, ontology constraint, evidence tracing, causal verification and dynamic updating.

The invention discloses a winter wheat yield forecasting method based on a random forest regression model, and relates to the technical field of winter wheat yield prediction.The winter wheat yield forecasting method comprises the steps of multi-source data integration, dynamic feature screening, random forest model optimization and yield prediction and verified.The multi-source data integration provides input data for dynamic feature screening; high-quality features screened by the dynamic features are used as training features of a random forest model optimization module, and a result of random forest model optimization is used as final yield prediction and verification of yield prediction and verification. According to the method, by designing multi-source data fusion and automatic model optimization, the problems that a traditional method is single in data source, difficult to capture complex agronomic relations and insufficient in model generalization ability are solved, high-precision and high-stability winter wheat yield forecasting is achieved, and powerful support is provided for agricultural production decision making.

The invention relates to the technical field of intelligent agriculture and computer vision, in particular to a small-sample high-density chicken counting framework based on a deep learning Mamba structure, which comprises a feature extraction network, a support-query enhancement module and a decoder. According to the method, a multi-scale feature extraction network based on a residual block (ResNet Block) is introduced, so that local detail information is effectively reserved; and then, a support-query enhancement module is constructed by introducing a Mamba structure, and context interaction between support features and query features is effectively enhanced by utilizing the long sequence modeling capability of linear complexity of the support-query enhancement module, so that the problems of individual overlapping and boundary fuzziness in a high-density chicken flock scene are solved. Experimental results on a PoultryCount real breeding data set show that the mean absolute error (MAE) and the root-mean-square error (RMSE) of the method are reduced compared with those of an existing method, and the chicken counting precision and generalization ability under the condition of a small number of labeled samples are remarkably improved.

The invention provides an agricultural pest diagnosis and prevention method and device, electronic equipment and a storage medium, and relates to the technical field of agriculture, and the method comprises the steps: extracting image features, shooting time and a shooting position from a crop pest image of a target region; inputting the image features into a multi-modal recognition model to obtain a first diagnosis result output by the multi-modal recognition model; inputting the shooting time and the attribution environment data corresponding to the shooting position into the multi-source data mining model to obtain a second diagnosis result output by the multi-source data mining model; and inputting the first diagnosis result and the second diagnosis result into a disease and pest knowledge map for fusion reasoning, and generating a plant protection medication management suggestion. According to the invention, through the multi-modal information fusion and step-by-step reasoning technology and in combination with the disease and pest knowledge graph, the accuracy and interpretability of disease and pest diagnosis and the individuation of the prevention and control scheme are realized.

The utility model belongs to the technical field of agriculture and forestry machinery, and particularly relates to a soil improvement device. Comprising a deep scarification gun, an air inlet pipe and a spray explosion tank, the lower end of the deep scarification gun is in interference fit with a deep scarification head, the deep scarification head is of a conical structure, an airflow channel is formed in the deep scarification gun, a spraying hole is formed in the lower end of the deep scarification gun, an air inlet is formed in the side wall of the upper end of the deep scarification gun, and the airflow channel is communicated with the spraying hole and the air inlet; the air inlet pipe comprises a hose and a metal pipe, one end of the hose is communicated with the metal pipe, the metal pipe is in interference fit with the air inlet, and the other end of the hose is communicated with the air outlet of the spray explosion tank. According to the scheme, through the integrated modeling design of the deep scarification gun, the drill rod, the deep applying rod and the deep applying head are connected instead of threads or bolts, unnecessary connecting structures on parts are reduced, meanwhile, connecting pieces are reduced, the mechanical property of the deep scarification gun is optimized, the durability of the parts is improved, and the service life of the deep scarification gun is remarkably prolonged.