149 results about "Leaf disease" patented technology

The invention discloses a lightweight rice leaf disease identification method based on YOLO target detection. The method comprises the following steps: S1, inputting a rice leaf disease image; s2, carrying out super-resolution reconstruction on the image by using an improved Real-ESRGAN algorithm; s3, inputting the picture into an improved YOLOv8n target detection algorithm; s4, obtaining disease spot category and position information; and S5, visualizing the information on the image. The invention relates to the technical field of target detection, and has the beneficial effects that image super-resolution reconstruction is carried out aiming at the problems that a rice leaf scab target is relatively small and an image acquired in real time is relatively fuzzy, so that the resolution of the small target is improved, and the definition and texture features are improved. On the basis of a super-division model Real-ESRGAN, a group of residual dense modules containing five layers of cavity convolution layers are designed to help the network to acquire receptive fields and information of different scales.

The invention provides a corn leaf disease and insect pest detection method and device, and relates to the technical field of crossing of agricultural disease and insect pest monitoring and target detection, and the method comprises the following steps: obtaining a disease and insect pest sample image, preprocessing the obtained disease and insect pest sample image, and obtaining image data with labels; dividing the image data with the labels into a training set, a test set and a verification set according to a sample distribution proportion; inputting the training set into a corn leaf disease recognition model to train the model, setting a loss function to evaluate the trained model, setting a sample distribution strategy, and dynamically adjusting a sample distribution proportion according to an evaluation result to obtain an optimal corn leaf disease recognition model; and using the optimal corn leaf disease identification model to carry out disease and pest detection on the corn leaf image. By adopting the corn leaf disease and insect pest detection method and the corn leaf disease and insect pest detection device, the position information and the category information of typical corn leaf diseases and insect pests can be accurately classified and positioned.

The invention relates to the technical field of disease and insect pest image analysis, in particular to an AI image recognition and grading method for field crop leaf disease and insect pests, which comprises the following steps: under the irradiation of a field fixed light source, synchronously acquiring a plurality of polarized reflection images around crop leaves at preset angle intervals; extracting a pixel polarization degree matrix of a leaf area in each polarization reflection image; inputting the pixel polarization degree matrix into a polarization transmission model, outputting a cuticle anomaly coefficient graph, and marking an area exceeding a preset anomaly threshold in the cuticle anomaly coefficient graph as a highlight display area; matching an infection type template library according to a highlight display area distribution mode in the abnormal coefficient graph; and calculating an infection intensity value by combining the diffusion gradient of the highlight area, and outputting a pest grade. According to the method, the boundary of the optical mutation region of the focus region is depicted, so that the physical interpretation of disease detection is improved, and distinguishable feature spaces are provided for different infection mechanisms (such as fungal growth layers and insect pest piercing and sucking points).

The invention provides a multi-task identification method for strawberry diseases and insect pests, and belongs to the technical field of strawberry leaf disease identification. The method comprises the following steps: marking an enhanced original strawberry leaf image, and constructing a multi-task training data set; inserting a double attention unit on a semantic segmentation model decoder as a segmentation network, constructing a detection network based on a lightweight detection model, inputting a segmentation mask output by the segmentation network into the detection network to construct a cascade model, and performing preliminary training; performing multi-task joint learning on the cascade model; based on the multi-task data set, a progressive training strategy is combined with a cosine annealing learning rate adjustment strategy, and final training is carried out on the cascade model after joint learning; and inputting a to-be-detected strawberry leaf image into the finally trained cascade model, and outputting a disease type and a severity level. According to the method, the recognition sensitivity of tiny disease spots can be effectively improved, and the generalization ability of the model to complex illumination and shielding scenes is enhanced.

A system for classifying apple leaf diseases using deep learning and feature fusion, consisting of: A data input module, which includes a data storage module, is configured to store a data set created using various data sources, with the data set containing apple leaf images being derived from the data sets "Apple Leaf 9", "Kashmiri Apple Plant Disease" and "Plant Village Apple Leaf"; a data preprocessing module configured to perform preprocessing of the newly prepared dataset of apple leaves, wherein the data preprocessing module is configured to perform data decoding, expansion, resizing, segmentation, scaling and color conversion of the input image data; a feature extraction module that is operationally connected to the data processing module and is configured to receive preprocessed data and transfer the preprocessed data to one or more convolutional neural network models for feature extraction; a feature fusion module configured to combine the extracted features from the Convolutional Neural Networks to develop a fused feature vector; a hyperparameter optimization module configured to optimize the hyperparameters of the feature extraction models by implementing a particle swarm optimization algorithm; a classification module configured to classify the fused trait vector into 13 apple leaf disease classes using a random forest classifier; and a user interface connected to the classification module, configured to display the classification results.

The invention discloses a classification method for multi-scale feature enhancement of rice leaf diseases, and relates to the field of agriculture. The method specifically comprises the following steps: collecting initial data of rice leaves, and generating an enhanced data set by adopting a frequency-space dual-domain collaborative enhancement strategy; collecting the initial data set and the enhanced data set to obtain a self-made data set; the omni-directional attention collaboration module and the multi-scale pyramid fusion module are fused and connected into a model with the core being Swin Transform, and a deep learning classification model ASTNet is formed; and importing the self-made data set into an ASTNet model, and carrying out classification experiments on the eight rice leaf feature states to obtain eight feature classification results. According to the rice leaf disease multi-scale feature enhancement classification method oriented to the complex agricultural scene, the classification precision and robustness of the model on seven rice diseases are remarkably improved through frequency-space double-domain data enhancement, an omnibearing attention cooperation mechanism and a multi-scale pyramid fusion mechanism.

The invention discloses an apple leaf disease segmentation method based on a lightweight dual-path network, an apple leaf disease segmentation device based on the lightweight dual-path network, an apple leaf disease segmentation device and a computer readable storage medium. The problems that an existing disease segmentation method is high in model complexity and insufficient in multi-scale recognition capability are effectively solved. The lightweight encoder adopts a depth separable convolution and channel recombination technology, so that the parameter quantity and the calculation complexity are greatly reduced while the feature extraction capability is maintained, and the model can be deployed on edge equipment such as an unmanned aerial vehicle and a field robot. The enhanced cavity space pyramid pooling module constructs abundant multi-scale receptive fields through multi-branch parallel cavity convolution with different expansion rates, and can capture feature information of initial tiny disease spots and later fused disease spots at the same time.

The invention discloses an apple leaf disease multi-classification method based on a complex environment background, and the method comprises the following steps: constructing a common data set FGVC8 and a self-built complex environment background data set SCEBD, and carrying out the preprocessing of collected image data; constructing a multi-scale down-sampling module MSDM by combining a plurality of down-sampling strategies; constructing a multi-scale feature extraction module MFEN, and capturing diversified feature information of apple leaf diseases through a multi-branch structure; an improved triple attention mechanism is introduced into the MFEN, and key feature information is further extracted; and establishing a lightweight fusion attention multi-branch network LCAMNet model. According to the apple leaf disease multi-classification method based on the complex environment background, the accuracy and robustness of disease recognition are improved, and the method is suitable for apple leaf disease classification tasks with limited resources and complex backgrounds and has good universality and high efficiency.

The invention discloses a tomato leaf disease and insect pest identification method, which comprises the following steps of: acquiring tomato leaf images in different planting environments by using a high-definition camera, performing data enhancement in modes of random overturning, rotation, brightness contrast adjustment, Gaussian noise addition and the like after the tomato leaf images are labeled by agricultural experts, and normalizing the images; constructing a recognition model which is based on a lightweight convolutional neural network such as MobileNetV3 and is fused with an SE attention mechanism module and an FPN multi-scale feature fusion module; dividing the data set into a training set, a verification set and a test set according to 8: 1: 1, training a model by adopting an Adam optimizer, adjusting parameters according to the performance of the verification set, evaluating the model by using the test set, and optimizing according to a result; during actual application, images are collected in real time, preprocessed and input into the model for recognition, and pest and disease type and position information are output and displayed for early warning. According to the method, the recognition accuracy rate exceeds 95%, the hardware requirement is reduced, the robustness and generalization ability are enhanced, real-time monitoring and early warning are achieved, and agricultural economic losses can be reduced.

The invention relates to the technical field of image recognition, in particular to a horticultural product fungal disease image recognition system and method, and the system comprises an image collection module, an image segmentation module, an anomaly detection module, a feature extraction module, a disease recognition module and a decision output module. In the prior art, the intensity of a light supplementing lamp is manually adjusted, and instantly changing illumination conditions (such as cloud layer shielding and tree shadow shaking) are difficult to deal with in an open-air plantation, so that leaf scab details are often submerged by strong light or dim light is blurred; according to the scheme, a closed-loop linkage system of ambient light perception and an annular LED is innovatively deployed, and the light supplementing intensity is reversely adjusted in a millisecond level by tracking ambient brightness in real time, so that purple gray hyphae of downy mildew in mist in the morning and anthracnose black spots under the noon burning sun present completely consistent color saturation and texture definition in imaging; and the problem of misdiagnosis caused by illumination fluctuation in the traditional method is thoroughly solved.

The invention relates to a potato leaf disease area positioning method, system and device.The method is applied to the potato leaf disease area positioning device.The method comprises the steps that a target infrared image of a potato leaf is segmented into a normal area and an abnormal area according to the environment temperature through a disease segmentation model obtained in advance; determining the water content of the target infrared image according to the leaf spectrum information through a pre-obtained water content prediction model; the leaf emissivity characteristic is calculated according to the water content through a pre-obtained temperature correction model, the temperature of the abnormal area is corrected based on the emissivity characteristic and the water content characteristic, and the temperature correction model is obtained based on potato leaf characteristic training; and through a pre-trained disease identification model, identifying a disease area from the abnormal area after temperature correction.

The invention discloses marine-derived penicillium as well as a fermentation method and application thereof. The marine source penicillium is named as Penicillium sp.DSF059, and the preservation number of the marine source penicillium is CCTCC (China Center For Type Culture Collection) NO: M 20232310. According to the invention, the culture conditions of the strain are optimized, the culture medium and culture conditions with high yield of antibacterial active compounds are selected for fermentation, more target products can be obtained, the operation is simple, the cost is low, and the method is suitable for industrial production. Meanwhile, the marine-derived penicillium sp. DSF059 can provide an excellent strain for the development of new drugs for resisting plant pathogens such as coffee leaf blight, coffee brown spot, grape gray mold, golden cypress brown spot, citrus black rot, phytophthora capsici, hickory leaf blight and coffee black spot as well as four pathogens separated from coffee leaf diseases and staphylococcus aureus; good application prospects are realized on prevention and treatment of phytopathogen and staphylococcus aureus.

The invention relates to the technical field of artificial intelligence, in particular to a rice leaf pest recognition method and device based on a convolutional neural network, and the method comprises the steps: obtaining a to-be-recognized rice leaf image; obtaining a preset rice leaf insect pest recognition model, wherein the preset rice leaf insect pest recognition model is trained through the target rice leaf insect pest image data set; and inputting the to-be-recognized rice leaf image into the preset rice leaf pest recognition model for pest recognition to obtain a target rice leaf pest recognition result corresponding to the to-be-recognized rice leaf image. According to the method, the accuracy of rice leaf disease and pest identification and the generalization ability of the model are improved, and the calculation cost in the training process is reduced.

The invention belongs to the technical field of crop disease and insect pest recognition, and particularly discloses a crop disease and insect pest recognition analysis method based on image processing, and the method comprises the steps: collecting a state image of a plant in a dynamic environment, and eliminating the interference of the dynamic environment on the state image recognition of a plant leaf; differentiated analysis is carried out on disease and insect pest characteristics of different areas corresponding to each leaf area, so that leaf disease and insect pest types and defect data thereof are accurately identified; the color value distribution health state of leaves near a leaf vein distribution area is detected by simulating the leaf vein distribution contour of the interference area, the corresponding leaf vein structure stability of each leaf area is analyzed, and the leaf state of the remaining area after the interference area and the pest and disease damage area of the leaves are removed is identified. And assisting in evaluating the leaf risk area condition in each leaf area, and judging the pest and disease damage state in the plant leaf according to the condition. The recognition precision of the crop state image is improved, and targeted prevention and control measures can be taken.

The invention discloses a corn leaf disease classification method based on improved ResNet18, and the method comprises the steps: obtaining a corn leaf image, and carrying out the preprocessing of the image, and obtaining an image data set; sending the image data into an improved ResNet18 neural network model for training, and classifying an output result by adopting a softmax function; and inputting a to-be-identified disease image into the trained improved ResNet18 neural network model for identification and classification to obtain corn leaf disease information in a corresponding category. According to the method, rich and meticulous feature expression is provided for corn leaf disease expression, the ability of the network to extract tiny scab features is improved, multi-scale features of a complex image space are captured, the model can have more attention to the scab area, the accuracy of corn leaf disease classification is improved, and the method is suitable for popularization and application. The corn disease identification accuracy is improved, the network parameters are reduced, and the model volume is reduced.

The invention discloses a grape leaf disease detection method and device and a program product, and relates to the technical field of image detection.The grape leaf disease detection method comprises the steps that a data image of a to-be-detected grape leaf is acquired; obtaining a pre-trained leaf disease detection model; wherein the leaf disease detection model is obtained by training based on an improved YOLOv8 lightweight grape leaf disease detection algorithm; and inputting the data image into the leaf disease detection model for identification to obtain a disease detection result. Due to the fact that disease detection is carried out through the leaf disease detection model constructed through the improved YOLOv8-based lightweight grape leaf disease detection algorithm, lightweight is achieved, meanwhile, high detection accuracy is kept, detection efficiency is improved, and grape leaf detection can still be efficiently carried out under the condition that computing resources are limited.

The invention belongs to the technical field of agricultural microorganisms, and relates to a bacillus velezensis strain S-34 and application thereof in prevention and treatment of mango leaf anthracnose, the bacillus velezensis strain S-34 is separated from rhizosphere soil of a mango variety 'Jinhuang' with high anthracnose resistance, is preserved in China Center for Type Culture Collection on May 29, 2025, and has a preservation number of CCTCC No. M 20251225. The bacillus velezensis S-34 provided by the invention not only can be used for preventing and treating mango leaf anthracnose, but also has a good antagonistic effect on various mango leaf pathogenic fungi. In addition, the bacillus velezensis S-34 as well as sterile fermentation liquor and volatile gas of the bacillus velezensis S-34 can inhibit spore germination and epidermis penetration processes of the colletotrichum siamensis, so that the infection process of the colletotrichum siamensis on plants is inhibited. The invention provides a novel antibiological inoculant and a preparation method for efficient prevention and treatment of mango leaf anthracnose and other leaf diseases in agricultural production, and is beneficial to green and healthy development of the mango industry.

The invention provides a crop leaf disease image recognition method, which comprises the following steps: collecting crop leaves to capture a scab area image, obtaining original pixel-level data and a boundary contour, and obtaining an initial data set containing a scab form; angle matching and direction comparison are carried out on the extracted contour coordinates and the main extension direction angle and a preset vein trend template, and the coincidence degree of the extension direction and the vein trend is evaluated; according to the coincidence degree of the expansion direction and the leaf vein trend, identifying the difference between the expansion rate of the disease spot along the leaf vein direction and the expansion rate vertical to the leaf vein direction, and analyzing to obtain a directivity judgment standard after dynamic adjustment; performing diagnosis specificity strong characterization identification classification processing on the morphological characteristics of the non-uniform disease spots according to the dynamically adjusted directivity judgment standard, and determining the tightness degree of judgment between the disease spot morphology and the pathogen species; and integrating the main expansion direction and the vein trend distribution data according to pathogen types, and generating a disease type discrimination report.

The invention discloses a rice leaf disease detection method and system based on improved YOLOv11, belongs to the technical field of agricultural intelligent detection, and solves the problems that fine-grained features are lost due to traditional downsampling, a spatial pyramid pooling module is limited in perception of diseases of different sizes, neck network feature fusion is insufficient, and the detection accuracy is poor. Therefore, the problems of complex background interference and low small target disease recognition precision are solved. According to the method, a data set containing eight types of rice leaf diseases is constructed, YOLOv11 is improved, a model is trained to detect the diseases, a PyQt5 visualization system containing six modules is further developed, and detection and prevention and treatment recommendation is supported. According to the method, the detection precision is high, the mAP of the improved model reaches 94.5% and is improved by 3% compared with the original YOLOv11, and the accuracy of diseases difficult to recognize is remarkably improved; the robustness is high, and the omission ratio is reduced by 2.5%; the system is simple to operate, supports multi-input and parameter configuration, provides a control strategy, and can be efficiently used for field detection.

The invention relates to the technical field of microorganisms, and discloses a Beauveria bassiana strain Bb20240406 and an application of the Beauveria bassiana strain Bb20240406 in prevention and control of diseases and insect pests, and the preservation number of the Beauveria bassiana strain Bb20240406 is CGMCC No.41802. The strain has high insecticidal activity on ground pests and underground pests of peanuts, and has a good control effect; the strain has a strong inhibition effect on pathogenic bacteria of peanut fruit rot, root rot, southern blight and leaf diseases, and has a good field control effect. In addition, the influence of the strain on the population number of orius sauliginosus is lower than that of a conventional chemical pesticide. When the strain provided by the invention is applied to production, the dosage and the use frequency of chemical pesticides can be reduced, and the problems of pesticide residues of agricultural products and environmental pollution are solved; the influence on the number of natural enemy populations is reduced, the farmland ecological environment is protected, and ecological balance is maintained.

The invention discloses a leaf disease and insect pest detection method based on multi-scale feature enhancement. Accurate detection of leaf diseases and insect pests is realized by constructing a multi-scale feature enhancement model and a fusion pyramid. The method comprises the steps of firstly collecting a leaf disease and pest data set and performing image preprocessing, and then constructing a leaf disease and pest detection model. The method comprises the following specific steps: introducing a DP-ACAM module, and effectively extracting feature information of leaf diseases and insect pests; a fourth minimum target network layer and a DAAM attention mechanism are newly added in the Neck network, a multi-scale receptive field is constructed through depth separable convolution, and context information of different scales is effectively captured; an original detection head is changed into an AMDA-Head detection head, attention fusion of different sizes and space and channel attention output are combined, and the recognition accuracy is effectively improved. And after model construction is completed, training, testing and performance evaluation are carried out. The method is suitable for an actual scene with densely overlapped leaves, changeable illumination conditions and diverse scab forms.

The invention discloses a method for constructing a diagnostic model for identifying leaf diseases. The method comprises the following steps: constructing a leaf disease original image set; preprocessing the leaf disease original image set to generate a leaf disease image sample set; the content of the leaf disease image sample set comprises leaf disease classification, leaf disease stages and a corresponding image set; the method comprises the following steps: defining a network model of which the basic structure is ResNet50, and adding an IB module to form a ResNet50-FIB structure; the IB module is used for performing multi-scale fusion and enhancement processing and outputting deep semantic features and spatial detail information, the deep semantic features are used for matching leaf disease classification, and the spatial detail information is used for matching leaf disease stages; the output layer outputs disease classification and disease stages according to image recognition; and training the network model, and constructing and generating a diagnosis model for identifying leaf diseases. According to the technical scheme, the bottlenecks of a traditional model in precision and speed balance, early disease misjudgment and cross-crop adaptability can be broken through, and the method has industrial popularization potential.

The invention relates to the technical field of pest control, in particular to a potato disease detection system based on image recognition. The system comprises an information acquisition module, an image preprocessing module, an unmanned aerial vehicle module, a data preprocessing module, a data management and analysis module and a remote interaction module. The information acquisition module comprises an image acquisition module and a meteorological sensing module; the unmanned aerial vehicle module provides a controllable mobile bearing platform; the data preprocessing module is carried on the unmanned aerial vehicle module and is used for monitoring potato leaf diseases in real time; the data management and analysis module is used for constructing a standardized training data set to carry out model optimization training; the remote interaction module is used for completing interaction work between the unmanned aerial vehicle and the ground. According to the invention, the image information and environmental parameters in the potato growth process are obtained through unmanned aerial vehicle inspection, and the abnormal areas are subjected to key screening, so that automatic detection and classification of potato diseases are realized, and the disease management efficiency of potatoes is improved.

The invention discloses a tomato leaf disease detection method, which is based on an improved lightweight target detection model MWD-YOLO11n to improve the accuracy and efficiency of disease identification. According to the method, three key improvements are carried out on the basis of an original YOLO11 model: firstly, a multi-scale dynamic attention mechanism (MSDA) is introduced, and the perception ability of the model to different-scale disease features is enhanced; secondly, wavelet convolution (WTConv) is adopted to replace part of a traditional convolution structure, and the capturing capacity of the model for disease edges and texture details is effectively improved; and finally, integrating a dynamic up-sampling operator (DySample), optimizing the resolution of the feature map, and further enhancing the characterization capability of the disease area. The method has high precision and low calculation amount, is suitable for deployment of edge equipment, shows excellent performance in tomato leaf disease detection tasks, and has good application prospects and popularization value.

The application discloses a marine source penicillium and a fermentation method and application thereof. The marine source penicillium is named Penicillium sp. DSF059, and the preservation number is CCTCC NO: M 20232310. Penicilliu The application optimizes the culture condition of the strain, selects a culture medium and culture condition with high yield of antibacterial active compounds to carry out fermentation, so that more target products can be obtained, the operation is simple, the cost is low, and meanwhile, the marine source penicillium Penicilliu Penicillium sp. DSF059 can provide an excellent strain for new drug development of resisting plant pathogenic fungi such as coffee leaf blight, coffee brown spot disease, grape gray mold disease, Chinese cinnamon brown spot disease, citrus black rot disease, pepper late blight, hickory leaf blight, coffee black spot disease and four pathogenic fungi separated from coffee leaf diseases and Staphylococcus aureus, and has a good application prospect for preventing and treating the plant pathogenic fungi and Staphylococcus aureus.

The invention relates to the field of grape leaf disease detection, and provides a grape leaf disease detection method based on YOLOv8, which comprises the following steps: acquiring original disease image data of grape leaves, preprocessing the original disease image data to obtain disease image data, and dividing the disease image data into a training set and a verification set; the method comprises the following steps: constructing a YOLOv8 basic model, and replacing a Bottleneck structure in a C2f module of the YOLOv8 basic model with a FasterNet network structure to obtain an initial grape leaf disease detection model; training, testing and optimizing the initial grape leaf disease detection model based on the training set and the verification set to obtain a grape leaf disease detection model; deploying the grape leaf disease detection model to a grape leaf disease detection system, and performing disease detection on the real-time grape leaf image data or the real-time grape leaf video data based on the grape leaf disease detection system to obtain a disease detection result. According to the invention, real-time disease detection of the grape leaf image or video data uploaded by the user is realized.

The application discloses a bacillus velezensis A13 wettable powder and application thereof, and belongs to the technical field of biological pesticides. The application aims to solve the problems of limited prevention and treatment effect of existing seed treatment, high labor intensity of foliar spraying and short effective period. The wettable powder comprises a mother powder prepared from fermentation liquor and diatomite, and a dispersant, a wetting agent, a stabilizer and a protective agent, wherein the total addition amount of the dispersant and the wetting agent is 5%-14% of the mass of the mother powder, the addition amount of the protective agent is 0.5%-2% of the mass of the mother powder, and the addition amount of the stabilizer is 1%-3% of the mass of the mother powder. The application can be used for preventing and treating root diseases caused by panax quinquefolius rust rot fungus and leaf diseases caused by panax quinquefolius alternaria leaf spot fungus, and promoting plant growth.

The invention discloses a rice leaf disease identification method used in an open scene, and relates to the technical field of computer vision, and the method specifically comprises the steps: 1, constructing an image enhancement set based on an antagonistic attention mask; 2, an entropy toughness index is defined, and a dynamic high-reliability sample library is constructed according to the index; and step 3, realizing steady-state linear transformation through dynamic weight distribution. And step 4, forming a final rice leaf disease identification model through the pre-training classifier after combination optimization and the linear transformation matrix. According to the method, by combining the antagonistic mask, the dynamic high-reliability sample library and the steady-state linear transformation, the problems that the model is poor in robustness and difficult in field self-adaption in an open farmland environment are effectively solved, and the performance of the rice leaf disease recognition model in an open scene is improved.

The invention discloses a light-weight tomato leaf disease detection method based on PCSE-StarNet, belongs to the technical field of plant disease detection, and aims at solving the problems that tomato leaf diseases are diversified in variety, uneven in erosion area, complex in environmental condition, limited in equipment computing power and the like. A convolution kernel extension technology is introduced in the initial feature extraction stage, so that the expression ability of feature information is enhanced; a new hybrid module PCSEBlock is provided, the module organically combines partial convolution (Pconv8), an SE attention mechanism and convolution solution, not only optimizes a feature extraction process, but also significantly reduces network redundancy, and further enhances feature extraction and representation learning ability through a channel extrusion mechanism. The method has the characteristics of high identification precision and lightweight structure, is suitable for tomato leaf disease detection in a resource-constrained environment, and has a great application prospect.

The invention discloses a rice leaf disease identification sampler, and belongs to the technical field of plant sampling. The rice leaf disease recognition sampler comprises a reciprocating mechanism and a first cutter, and further comprises a second cutter which is of a polygon prism structure, and when the reciprocating mechanism drives the first cutter to move towards one side close to the second cutter, a cutting edge of the first cutter can be attached to the side wall, close to one side of the first cutter, of the second cutter; the intermittent rotating mechanism is used for driving the second cutter to rotate until the next side wall of the second cutter faces the first cutter when the cutting edge of the first cutter is separated from the side wall of the second cutter; the brushing mechanism comprises a first brush and a disinfectant spraying part, and the disinfectant spraying part is used for spraying disinfectant to the first brush. The rice leaf disease identification sampler can prevent tissue fluid or cell fluid of diseased leaves from remaining on the cutting knife when sampling the diseased leaves of rice, thereby preventing rice viruses from forming cross infection among different kinds of rice.