464 results about "Agricultural information" patented technology

The invention relates to the technical field of agricultural information perception and decision making, in particular to a multi-source perception fused agricultural monitoring method and system. The method comprises the following steps: converting a multi-source heterogeneous agricultural sensing signal into a space-time tensor, constructing a semantic resonance field to simulate nonlinear coupling between modals, and driving multi-modal data to adaptively aggregate by using a gravitational evolution mechanism to form a fused semantic field; calculating non-linear response to generate an agricultural state emergence index, and according to the index, identifying a potential risk area and constructing a binary risk map; for the risk area, semantic disturbance is mapped into an agricultural variable disturbance vector through a modal decoupling matrix, a minimum intervention strategy is generated in combination with sparse optimization of an operation response matrix, and feasible operation suggestions are output after verification of an agricultural knowledge graph; a drift potential energy function is constructed based on strategy execution feedback, strategy parameters are dynamically updated through gradient descent, and closed-loop self-evolution optimization is achieved in combination with trend prediction. According to the invention, full-link adaptive optimization from multi-source sensing to regulation and control decision is realized.

The invention relates to the technical field of agricultural information management, and particularly discloses an agricultural information management system and method based on a big data platform, and the method comprises the steps: firstly deploying a multi-source data collection module at an edge calculation node, and obtaining and standardizing the soil moisture content, meteorological environment and equipment operation data in real time; secondly, constructing a local dynamic irrigation strategy model, and realizing multi-objective optimization through a reinforcement learning algorithm; establishing a federated learning framework at the cloud, dynamically distributing node weights by adopting an attention mechanism, and realizing model aggregation of privacy protection in combination with secure multi-party computing; an optimal irrigation instruction is generated through a multi-source data fusion engine, and a three-level response exception handling mechanism is established; and finally, a closed-loop feedback system containing short-term incremental learning and long-term architecture optimization is formed. The corresponding management system comprises six functional modules, namely a data acquisition module, a local modeling module, a federated learning module, a real-time decision-making module, an abnormal monitoring module and a closed-loop optimization module.

The invention discloses a remote sensing image cultivated land segmentation method and system fusing context and boundary perception, and belongs to the technical field of remote sensing image processing and agricultural information. Constructing a cultivated land segmentation initial model composed of a backbone network, a feature enhancement module, a multi-scale feature fusion de-wharf module and a mask prediction module; training set data are input into the initial model, a composite loss function value is calculated, back propagation is executed, and a cultivated land segmentation model with boundary sensing ability is obtained through multi-round iterative optimization; and inputting the remote sensing image into the trained cultivated land segmentation model, and outputting a binary segmentation image representing the cultivated land position. Visual state space modeling and large receptive field convolution are combined, deep and shallow layer information is fused through feature injection, boundary perception supervision and composite loss are introduced, cultivated land boundary discrimination is improved, remote sensing image cultivated land high-precision extraction is achieved, and the method is suitable for agricultural interpretation and monitoring.

The invention relates to the field of agricultural information processing and artificial intelligence, in particular to a farmland yield prediction method and system based on a heterogeneous graph neural network, and the method comprises the steps: obtaining multi-source farmland data, and extracting an initial feature vector of a farmland plot node; on the basis of the initial feature vector, constructing a heterogeneous graph structure containing multiple semantic relationships; performing node feature updating on the heterogeneous graph structure by using a heterogeneous graph neural network, and dynamically aggregating information of multiple types of neighbor nodes through relation-aware message passing and an edge propagation gating mechanism; performing enhancement processing on the node features by using a space-time dependency enhancement mechanism and a knowledge-guided reasoning mechanism; and outputting a regression prediction result of the farmland yield through a prediction module based on the enhanced node features. The invention aims to realize modeling and intelligent yield prediction based on multi-source heterogeneous data in an agricultural system, and improve the environmental adaptability, prediction generalization ability and interpretability of farmland yield prediction.

The invention discloses a garlic disease and insect pest dynamic diagnosis system based on temperature and humidity time sequence data, relates to the technical field of agricultural information processing, and solves the problems that in the prior art, a fixed parameter model is prone to sensitivity sudden drop, false alarm sudden increase and systematic deviation under sudden or semi-sudden changes of field environment and management variables. According to the scheme, semantic processing of temperature and humidity and farming events is carried out through an acquisition module, a system coupling module carries out segmented identification on distribution mutation caused by environment and operation, a domain representation module constructs causal threatening features, a prediction calibration module carries out dual-path drift decomposition and rapid correction, and a prediction result is obtained. The sample adding and label collecting module generates anti-fact samples and actively collects labels, and the decision attribution module outputs a structured evidence chain; according to the method, the dynamic adaptive capacity and reliability of the diagnosis system under the conditions of non-stationary distribution and concept drift are remarkably improved.

The invention discloses a traditional Chinese medicinal material standard base intelligent irrigation and fertilization method based on the Internet of Things and a decision making system, and relates to the technical field of agricultural informatization. Through multi-source data acquisition and intelligent analysis, the limitation of an existing irrigation and fertilization method is solved, accurate decision making and resource optimization are realized, comprehensive data are acquired by utilizing a soil moisture content sensor, a meteorological station and plant image acquisition equipment, and by combining principal component analysis, a long-short-term memory neural network and a block chain technology, the intelligent irrigation and fertilization method is realized. According to the system, the water and fertilizer requirements are accurately predicted, an irrigation and fertilization scheme is optimized, data credibility and traceability are ensured, meanwhile, real-time control and collaborative operation of equipment are achieved through edge calculation and a wireless sensor network, the resource utilization efficiency is improved, the production cost is reduced, and scientificity and sustainability of traditional Chinese medicine planting are remarkably improved.

The invention relates to the technical field of agricultural information, in particular to a tobacco agriculture standard named entity recognition method and system based on a hybrid neural network, and the method comprises the steps: carrying out the embedded representation of an input tobacco agriculture standard text through a BERT pre-training language model, and generating a word vector sequence containing global semantic information; performing local feature extraction on the word vector sequence by using an iterative expansion convolutional neural network to obtain a local feature vector; splicing the global semantic information and the local feature vectors, and inputting the spliced global semantic information and local feature vectors into a bidirectional long-short-term memory network for context feature extraction to generate context enhancement features; weight optimization is carried out on the context enhancement features through a multi-head attention mechanism, and key semantic features are highlighted; and performing label prediction on the optimized feature sequence by adopting a conditional random field decoder, and outputting a standard article element entity identification result. According to the method, high-precision and high-robustness named entity recognition is realized, and the method is particularly suitable for complex semantic and low-resource field scenes.

The invention discloses an intelligent wheat yield prediction method and system, and relates to the technical field of agricultural information. According to the method, basic geography, climate, soil, crop physiology and agricultural management multi-source data are collected, and an input feature set is obtained through preprocessing and feature engineering; constructing an Attention-LSTM-CNN model fused with an improved attention mechanism, extracting local features through CNN, capturing time sequence association through LSTM, highlighting key contribution through an attention layer, and finishing model training in combination with an RMSE loss function and an Adam optimizer; and dynamically updating data and a prediction result in a wheat growth cycle, and outputting and triggering early warning in multiple forms. The system correspondingly comprises a data acquisition module, a data storage module, a data preprocessing module, a model calculation module, a prediction output and early warning module and a communication module. The method solves the problems that a traditional method is single in data, poor in model adaptability and lack of dynamic prediction, high-precision full-period prediction is achieved, and scientific support is provided for agricultural decision making.

The invention belongs to the technical field of remote sensing image intelligent processing and agricultural information extraction, and particularly relates to a remote sensing large model pre-training and multi-granularity boundary supervision natural field type extraction method, which comprises the following steps: firstly, pre-training a model on a large-scale space-time spectrum remote sensing data set, and combining anchor point sensing mask and geographic information coding; secondly, inputting the multi-scale features into a multi-branch structure sensing network, and outputting a semantic segmentation prediction map through high and low resolution double input and dynamic attention fusion; generating a multi-scale field boundary label through morphological operation, and outputting a boundary prediction map through multi-task supervision after domain enhancement of features by a frequency space double-domain enhancement module; and finally, aligning the two images and performing pixel-level operation to obtain a high-precision extraction result. Through the method, the global classification error during cross-region migration is greatly reduced, the method is adaptive to a low-pixel wide ridge, the boundary detection value and the closure rate of a small-scale field are improved, and the extraction precision of a large field and a small field is considered.

The invention belongs to the technical field of agricultural information, and discloses a rape variety environmental adaptability evaluation system based on machine learning. The system is composed of a data acquisition standardization unit, a regional environment feature modeling unit, a variety phenotype and pedigree association unit, a feature screening and model training unit, a regional adaptability scoring and decision-making unit, a dynamic learning and evolution updating unit and an evaluation visualization and variety recommendation unit. By performing standardization processing on multi-source data, format and dimension differences are eliminated, and a high-quality data foundation is laid for subsequent analysis. Fusing environment and variety bilateral characteristics, and enabling the model to capture environment influence and variety heredity characteristics at the same time. Advanced methods such as ensemble learning and a time sequence neural network are adopted, and the complex relation is accurately mined. Compared with a traditional field test and the prior art, errors are greatly reduced, the adaptation degree of the variety in different areas is more accurately judged, a scientific basis is provided for planting recommendation, and the yield and quality of the oilseed rape are improved.

The invention discloses a crop disease and insect pest image recognition algorithm based on dynamic adaptive multispectral fusion Transform, and belongs to the crossing field of agricultural information technology and computer vision. The objective of the invention is to solve the problems of insufficient multi-spectral feature fusion, poor complex background adaptability and insufficient precision in traditional recognition. Acquiring pest and disease damage images of crops in different wave bands (visible light, near-infrared light and the like) to construct a data set; through a dynamic adaptive fusion module, spectral weight distribution is learned in real time based on an attention mechanism, weights are adjusted according to spectral response differences of disease and insect pest areas, and accurate feature aggregation is achieved; the fusion features are input into an improved Transform model, a self-attention mechanism of crop semantic priori knowledge is introduced, focusing of key features of diseases and insect pests is enhanced, and background interference is inhibited; and finally outputting the disease and pest category and confidence. According to the method, through dynamic fusion and Transform cooperation, the recognition accuracy and robustness in a complex scene are improved, support is provided for early warning and prevention of diseases and insect pests, and the application value is remarkable.

The invention discloses a sensor-based agricultural information data acquisition system and method, and belongs to the technical field of agricultural information acquisition. Multi-type sensor nodes are arranged in an agricultural target area in a heterogeneous manner; the method comprises the following steps: constructing a multi-dimensional influence factor model according to crop growth stages and environmental historical fluctuation data, dividing initial sensing sub-regions, and configuring a sensor cluster; dynamically updating the sensing boundary based on the historical change rate and the spatial gradient information; fusing the heterogeneous data by adopting a multi-channel time synchronization mechanism to generate a standardized environment vector set W; calculating a sampling priority matrix according to a parameter change trend in the W, and adaptively adjusting a node state; integrating an energy consumption estimation model, and executing low-power-consumption scheduling; when any parameter exceeds the threshold, high-density sampling and remote early warning are triggered; according to the method, high-precision, low-power-consumption and dynamic-response data acquisition and intelligent early warning can be realized, and the efficiency and reliability of an agricultural sensing system are improved.

The invention discloses a method and a system for predicting diseases and insect pests of saline-alkaline tolerant rice based on artificial intelligence. The method comprises the steps of collecting rice data, optimizing the rice data, establishing a prediction model for the diseases and insect pests of the saline-alkaline tolerant rice and predicting the diseases and insect pests of the saline-alkaline tolerant rice. The invention belongs to the technical field of agricultural information, and particularly relates to a method and a system for predicting diseases and insect pests of saline-alkali tolerant rice based on artificial intelligence. According to the scheme, rhombic plot segmentation is dynamically adjusted according to the irregular terrain and the saline-alkali accumulation area boundary of the saline-alkali tolerant rice field, and the collaborative weight is calculated based on the saline-alkali stress degree and the disease and insect pest risk level; key region learning is enhanced in combination with a plot total score and a selection probability formula; a plot score, a neighborhood pest and disease density mean value and a neighborhood saline-alkaline gradient are introduced into a spatial branch, so that the capturing capability of spatial correlation is improved; on the basis of the dynamic adjustment tolerance threshold, the influence of the extreme value is limited through the loss upper limit coefficient, a pest and disease prediction error regulation and control function is constructed, and the pest and disease prediction accuracy is improved.

The invention relates to the technical field of agricultural informatization and intelligent decision making, and discloses a vegetable growth cycle pest control decision making system and method based on a knowledge graph. According to the method, an unstructured agricultural data stream including vegetable varieties, growth stages, environmental parameters, pest and disease records and geographic information is collected from an original database. A data tuple set with a time stamp is generated by performing scanning identification and content extraction on a data stream. Then, clustering is carried out according to the growth stages to which the tuples belong, and stage feature clusters sorted according to the growth cycle are formed; and performing multi-dimensional feature correlation analysis on the stage feature cluster, and establishing a dynamic coupling relationship among the environmental parameters, the disease and pest history and the geographic space features under the growth cycle dimension. And based on the dynamic coupling relationship, generating disease and pest control decision parameters of the vegetables in the specific planting area in each growth cycle stage. According to the invention, accurate matching between the control decision and the crop physiological time sequence and regional characteristics is realized.

The invention discloses a construction method of a soil microorganism nitrogen metabolism function prediction model, and relates to the technical field of environmental microbiology and agricultural information, and the method comprises the following steps: S1, constructing a spatial-temporal heterogeneity representation engine, and dynamically decoupling a multi-level gradient of a soil environment through an adaptive spatial-temporal mesh generation algorithm; according to the construction method of the soil microorganism nitrogen metabolism function prediction model, the adaptive capacity of the soil microorganism nitrogen metabolism function prediction model in a complex environment is improved, and multi-level environment gradient dynamic analysis from a centimeter-level rhizosphere microdomain to a kilometer-level landscape scale is realized; the problem of cross-scale feature fusion caused by temporal-spatial resolution mismatch of a traditional model is solved, the simplified hypothesis of a traditional statistical model on the synergistic-antagonistic relationship of microbial functional genes is broken through, a dynamic coupling mechanism of a nitrogen metabolism path under environmental disturbance is accurately quantified, and the prediction error is reduced compared with the prior art.

The invention provides a crop growth prediction method and system based on multi-modal data, and relates to the technical field of agricultural information, and the method comprises the steps: 1, collecting real-time environment data and crop physiological data of a heterogeneous multi-source sensor in a greenhouse environment, and constructing a multi-modal original data set; 2, performing preprocessing and space-time alignment on the multi-modal original data set to form a multi-modal data fusion matrix with a unified timestamp; step 3, based on the multi-modal data fusion matrix, selecting a reference data feature set, generating an environmental evolution mode and a physiological evolution mode, and calculating a correlation degree between the two modes to determine a feature response dimension; and respectively selecting dynamic monitoring feature sets inside and outside the feature response dimension, constructing a feature evolution trajectory according to a time sequence evolution relationship, and generating a dynamic compensation coefficient. By integrating environment and crop physiology multi-source data, the association rule of crop growth and environment is predicted, and the agricultural production efficiency is improved.

The invention discloses a crop pest image intelligent identification system based on an electronic information technology, and relates to the technical field of agricultural informatization. The system comprises an image acquisition module which acquires RGB image data of a target crop area and synchronously acquires associated environment monitoring data and image shooting time data; the preprocessing module preprocesses the data; the insect pest recognition module outputs insect pest type data and insect body position frame selection coordinates through an improved YOLO recognition model; the comparison module generates insect pest development state data; a risk assessment module calls a pest hazard level dictionary matching risk coefficient, and calculates an environmental suitability value in combination with environmental monitoring processing data; the risk scoring module generates a comprehensive risk score; and the early warning decision module matches the prevention and treatment scheme data when the comprehensive risk score exceeds a preset threshold. According to the technology, intelligence and precision of insect pest recognition and early warning are realized, and the agricultural insect pest prevention and control efficiency is improved.

The invention discloses a dynamic prediction method for the yield of breeding manure, and relates to the technical field of agricultural information.The method comprises the steps that farm real-time data, manure returning historical data and external auxiliary data are synchronously obtained through multi-source data collection and preprocessing, and feature vectors are formed through cleaning and feature engineering; a dynamic prediction model is constructed based on an LSTM neural network and an attention mechanism, the LSTM captures time sequence dependence through a gating mechanism, and the attention mechanism weights and focuses key features; every set period, calculating a prediction error by utilizing newly acquired data, updating model parameters by adopting an incremental gradient descent method and correcting a prediction result; and engineering deployment is realized through edge calculation, data security transmission and performance monitoring, a complete closed loop from data acquisition, model prediction to result correction is formed, and prospective decision support is provided for resource utilization of planting and breeding feces.

The invention relates to the technical field of agricultural information, in particular to a crop yield prediction method, a verification method, a computer system and a readable storage medium. Standardized moisture productivity, a crop coefficient before canopy aging, a harvest index, a maximum canopy coverage degree, a canopy growth coefficient, a canopy attenuation coefficient, a minimum rooting length and a maximum rooting length are selected as analysis parameters, and the main effect of crop model parameters is quantified through a first-order sensitivity index. The interaction effect of crop model parameters is quantified through the global sensitivity index, and the complexity of a parameter system is reduced; a differentiation calibration strategy is constructed based on sensitivity grading, a traditional all-parameter parameter adjustment mode is replaced, and the complexity of a parameter system is remarkably reduced. The objective of the invention is to solve the problem of how to improve the crop yield prediction precision of a crop model in a climate-variable region.

The invention discloses a potato seedling grading environment parameter dynamic optimization regulation and control method, and relates to the technical field of agricultural informationization. Based on an Internet of Things sensor deployed in a target area, stem diameter, chlorophyll content and plant height data of potato seedlings are collected, the seedlings are divided into weak seedlings, middle seedlings and strong seedlings by adopting K-means clustering, and the weak seedlings, the middle seedlings and the strong seedlings are classified into a weak seedling classification model, a middle seedling classification model and a strong seedling classification model; setting the duration of the initial light period; and carrying out gradient photoperiod test on each type of potato seedlings by taking the stem elongation rate and the chlorophyll content as constraint conditions. Through grading photoperiod modeling and ant colony algorithm dynamic optimization, the limitation of traditional fixed photoperiod regulation and control is broken through, the initial photoperiod range is set based on the seedling physiological difference, and the photoperiod-growth rate response curved surface model is constructed in combination with the stem elongation rate and chlorophyll content constraint conditions. The influence of different photoperiod combinations on seedling growth is quantified, accurate matching of photoperiods and seedling requirements is ensured, excessive growth or premature senility is avoided, and the growth rhythm stability and the resource utilization efficiency are improved.

The invention provides an early warning method and system for greenhouse vegetable diseases and insect pests, and relates to the technical field of agricultural informationization. Environmental parameters and vegetable images are collected, a plant distribution network is established, and the propagation influence degree is calculated; analyzing an environmental suitability index of a pest propagation mechanism based on the environmental parameters; extracting disease features by using multi-source image fusion to determine an initial focus; calculating the propagation probability by combining the propagation influence degree and the environmental suitability degree, and constructing a dynamic propagation path prediction map; determining a prevention and control priority and generating partition early warning information; finally, relevant parameters are optimized according to the actual prevention and control effect, and accurate and timely disease and pest early warning is achieved.

The invention discloses a method for accelerating voxel volume measurement through multi-sensor fusion, and relates to the technical field of agricultural information, and the method comprises the following steps: S1, point cloud collection at different time points; s2, carrying out preprocessing and unified spatial reference on the fusion point clouds at different time points, constructing a time period three-dimensional reconstruction model, and obtaining an accurate volume variation through an external calibration method; s3, voxelization processing is carried out on the model, calculation is carried out under the condition that the resolution is reduced, and a coarse voxel volume change initial value of the time period is obtained; and S4, inputting the coarse voxel volume initial value and the multi-sensor feature difference value into the neural network model, and outputting a high-precision volume change result. According to the method for accelerating voxel volume measurement through multi-sensor fusion, the calculation complexity and memory consumption are remarkably reduced, meanwhile, the precision and real-time performance of volume change measurement are improved through multi-sensor fusion and network correction, and the method is suitable for dynamic volume monitoring in agriculture, storage and logistics management.

The invention relates to the technical field of agricultural informatization and supply chain risk control, and particularly discloses an agricultural supply chain risk intelligent sensing and early warning system based on multi-source data fusion. According to the system, six multi-source data including a producing area environment, an agricultural product category, a transaction behavior, a logistics link, credit and market information are integrated through a data acquisition layer; space-time alignment and cleaning are carried out through the data processing and alignment layer; through feature engineering and a multi-source fusion layer, depth features are constructed by comprehensively utilizing a knowledge graph and a graph attention network, Transform behavior sequence modeling, CNN-LSTM remote sensing time sequence analysis and Graph2Seq logistics trajectory prediction, and the Transform and the graph attention network are specially adapted for an agricultural scene; the dynamic risk scoring layer is used for comprehensively calculating five risks of producing areas, logistics, transactions, markets and credit, and a comprehensive risk score is output through weighted fusion; a decision basis is provided by integrating SHAP, attention visualization and map path tracking through an interpretability output layer; and finally, outputting a grading risk strategy by a strategy decision-making layer. According to the invention, dynamic, accurate and explainable intelligent assessment and early warning of full-link and multi-dimensional risks of the agricultural supply chain are realized.

The invention provides an insect pest prediction method based on PSO-LSTM, belongs to the technical field of agricultural information, and aims to solve the problems that a single machine learning model is adopted in a traditional insect pest prediction method, the long-term modeling capability of time sequence data is insufficient, the complex nonlinear relation between the number of insect pests and meteorological factors is difficult to capture, and the prediction precision is low. Comprising the following steps: S1, collecting insect pest data and establishing a multivariable time sequence data set; s2, data preprocessing; s3, feature selection; and S4, establishing an insect pest prediction model, optimizing the insect pest prediction model by using a PSO module, and obtaining a prediction value of the number of insect pests at the next moment based on the optimized insect pest prediction model.

The invention discloses a black land quality comprehensive evaluation system, which relates to the technical field of agricultural information and comprises a data acquisition module, a data processing module, an evaluation system construction module, a weight setting module and a comprehensive evaluation module. The data acquisition module is used for collecting multi-aspect data of black land, wherein the multi-aspect data comprises soil physical property data, soil chemical property data and land utilization and management data. The invention further discloses a black land quality comprehensive evaluation method. According to the comprehensive evaluation system and method for the black land quality, comprehensive evaluation of the black land quality is achieved by comprehensively collecting information in multiple aspects of soil physical properties, chemical properties, land utilization and management data and meteorological data, and the comprehensive evaluation mode is more accurate and comprehensive compared with traditional single index evaluation; and the actual condition of the black land can be reflected more truly, so that the evaluation is more in line with the actual agricultural production condition.

The invention discloses a high-standard farmland construction time sequence optimization method, and relates to the technical field of agricultural information, and the method comprises the steps: superposing a permanent basic farmland distribution diagram layer and a high-standard farmland distribution diagram layer, and recognizing a permanent basic farmland region which is not constructed into a high standard; obtaining multi-source basic data in the region, and representing the grain yield by adopting an LAI average value after S-G filtering; constructing a high-standard farmland construction ecological toughness evaluation index system; the combined weight of each evaluation index is calculated according to the combination of an entropy weight method and a CRITIC method, and a set pair analysis model is established to calculate the farmland ecological toughness; a spatial interaction relationship between grain yield and farmland ecological toughness is obtained based on a bivariate local spatial autocorrelation analysis method, a preferential plot of high-standard farmland construction is identified in combination with a self-organizing mapping neural network and a K-means method, and a construction time sequence arrangement and spatial layout optimization scheme is formed. According to the invention, the problem of high-standard farmland construction priority can be solved.

The invention relates to the technical field of agricultural information and seed and seedling cultivation, and discloses a peanut planting method for relieving continuous cropping obstacles of peanuts. The method comprises the following steps: constructing initial digital twins of a land parcel, and synchronously constructing a disease-resistant peanut variety screening and seedling pretreatment system; historical continuous cropping information, soil physicochemical and microorganism data, meteorological time sequence data and seedling cultivation parameters are fused, the system is driven to evolve into operation state digital twins, and seedling-soil-environment collaborative simulation is achieved. Evaluating the continuous cropping obstacle risk and the seedling adaptability based on the twinborn body, and generating a comprehensive diagnosis report containing a nutrient imbalance index, pathogenic bacteria abundance prediction, a root exudates cumulative effect and a seedling disease-resistant adaptation coefficient; according to the method, differentiated seed and seedling optimization, soil improvement and cultivation strategies are implemented, and management measures are dynamically optimized through real-time data feedback in a growth cycle. Through cooperative driving of digital twinning and seed and seedling cultivation, source prevention, control and intervention of continuous cropping obstacles are achieved.

The invention discloses an early-stage intelligent monitoring system for citrus reiculata Blanco canopy diseases and insect pests based on multispectral imaging, and belongs to the technical field of agricultural information. The system comprises a multispectral imaging module, a three-dimensional point cloud acquisition module, a data fusion module, a time sequence data analysis module, an intelligent identification module and a monitoring result output module. The method comprises the following steps: synchronously acquiring a multispectral image and laser radar point cloud data of a citrus reiculata tree canopy, and generating a point cloud model through spatial registration fusion; continuously recording model data of a plurality of time points, and extracting a time sequence feature vector; identifying disease and pest types and severity by using a deep learning model; and outputting a result to the user terminal. According to the invention, the problem that large-range and high-precision early monitoring of diseases and insect pests of citrus reiculata canopies is difficult to realize in the prior art is solved, early discovery, precise positioning and trend early warning of the diseases and insect pests are realized through air-space-ground integrated data fusion and intelligent analysis, and the intelligent level of orchard management and the disease and insect pest control efficiency are effectively improved.

The invention discloses a monitoring analysis system and method for potato breeding, and relates to the technical field of agricultural information technology and intelligent breeding, and the system comprises a multi-modal data acquisition module which is configured to synchronously obtain greenhouse environment parameters, potato plant multispectral imaging data and leaf metabolite concentration data; and the space-time causal modeling module is connected to the multi-modal data acquisition module, and is used for constructing a dynamic correlation model among environmental fluctuation, gene expression and phenotypic characteristics, and deducing and quantifying the genetic effect of the environmental abnormal event based on anti-fact intervention. According to the monitoring analysis system and method for potato breeding, through multi-modal data fusion and space-time causal modeling, the accuracy and early warning timeliness of potato breeding monitoring are improved, the limitation of traditional visible light imaging is broken through, and the incubation period disease recognition accuracy is improved compared with a traditional method.

The invention provides an unmanned aerial vehicle tea tree disease detection method based on morphological perception, and belongs to the technical field of agricultural information and computer vision. The method comprises the steps of firstly collecting tea garden images and constructing a data set; then, a target detection network embedded with a differentiable morphological sensor module is constructed, and the differentiable morphological sensor module extracts multi-scale shape features by using differentiable morphological operation; an adversarial learning mechanism is introduced in training, and the distinguishing ability of the model on disease and health areas is enhanced through a discriminator; after the training is completed, mining a difficult case sample based on the cosine distance between the morphological characteristics and the disease prototype vector, and carrying out supplementary training; and finally, integrating a plurality of models with optimal performance, and generating a final detection result through weighted reasoning. The method effectively strengthens the perception capability of the model for the subtle morphological characteristics of the diseases, solves the problems of low disease detection precision and insufficient difficult sample learning in a complex tea garden background, and remarkably improves the detection accuracy and robustness.