63 results about "Leaf disease" patented technology

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.

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 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 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 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 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 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 grape leaf disease intelligent classification method based on computer vision, and relates to the technical field of agricultural intellectualization, and the method comprises the steps: carrying out the preprocessing of visible light images according to a fixed sequence; establishing a pixel-level model in the HSV space and the LAB space, and fusing to generate a leaf mask; block modeling, low-bit robust projection and asynchronous block attention of sparse masks are carried out based on masks; features are formed through mask-guided channel attention fusion, classification is completed, and area and edge constraints are applied; multi-modal entropy weight gating is constructed, and a color space weight is returned in a closed loop mode; federated training and aggregation are adopted, and end side recalibration and rollback processing are completed. According to the method, stable and reproducible disease classification output is realized under the condition that privacy and computing power are limited.

This invention discloses a method for detecting rice leaf disease lesions based on the computing power of mobile devices. It utilizes an improved detection model based on YOLOv5 to detect rice diseases. This detection model introduces a ternary attention mechanism to enhance the attention feature extraction capability of YOLOv5, replaces the C3 module in the neck network of YOLOv5 with a C3CBAM module, and replaces the convolutional Conv in the backbone network of YOLOv5 with a lightweight convolutional GhostConv. Deployed on a Raspberry Pi mobile device and accelerated using MNN, the method detects input rice leaf images and obtains the detection results. This invention maintains the detection accuracy for common rice leaf diseases while maintaining a relatively fast inference speed on a mobile Raspberry Pi.

The utility model relates to the technical field of disease recognition devices, in particular to an apple leaf disease recognition device carried by an unmanned aerial vehicle, which comprises an unmanned aerial vehicle platform, an image acquisition camera and a mounting mechanism, the mounting mechanism comprises a mounting seat, a sliding rail, a clamping ring and two fixing assemblies, and each fixing assembly comprises a mounting plate, a limiting rod and a telescopic sleeve. The mounting seat is provided with a plurality of limiting holes, the image acquisition camera is arranged below the unmanned aerial vehicle platform, the mounting seat is slidably connected with the unmanned aerial vehicle platform, the sliding rail is fixedly connected with the unmanned aerial vehicle platform, the clamping ring is fixedly connected with the mounting seat, the clamping ring is slidably connected with the sliding rail, the image acquisition camera is fixedly connected with the mounting seat, and the mounting plate is fixedly connected with one end of the sliding rail. The limiting rod is slidably connected with the mounting plate, and the image acquisition camera can be dismounted and mounted below the unmanned aerial vehicle platform through the mounting mechanism, so that an operator can conveniently maintain the image acquisition camera, and the practicability of the device is further improved.

The application relates to the technical field of disease and pest detection, and particularly discloses a tomato leaf disease and pest detection method based on an improved YOLOv8, which comprises the following steps: constructing a tomato leaf image dataset containing each type of disease and pest; fusing a GhostNet and an RFA attention mechanism, modifying a loss function into a Wise IOU, and constructing an improved YOLOv8 model; configuring training hyperparameters, updating model weights based on the tomato leaf image dataset, and obtaining a trained improved YOLOv8 model; performing disease and pest detection on a to-be-detected tomato leaf image based on the trained improved YOLOv8 model, and obtaining a disease and pest detection result of the to-be-detected tomato leaf image; and the method can improve the efficiency and real-time performance of the model, reduce the number of parameters, enhance the attention ability of the model to features, and improve the detection precision, so that more accurate disease and pest detection can be realized.

The invention discloses a cross-variety plant leaf disease recognition method and system based on multi-source knowledge fusion, and belongs to the technical field of computer vision and plant disease recognition. According to the method, a multi-source feature extraction network is constructed to perform feature extraction on data of each source domain, and source domain features are adaptively adjusted based on a variety difference perception mechanism; a knowledge quality evaluation module is constructed to calculate the knowledge credibility score of each source domain, a dynamic weight fusion strategy is adopted to distribute a sample-level adaptive weight for each source domain according to the knowledge credibility and the sample feature similarity, a cross-domain alignment network is constructed to realize feature distribution alignment, a joint loss function training model is generated, and the self-adaptive weight of each source domain is obtained. According to the method, the problems of negative migration, poor cross-variety generalization ability and the like caused by insufficient self-adaptive utilization of multi-source information in a single-source domain and lack of knowledge quality evaluation are effectively solved, and the accuracy and robustness of cross-variety plant leaf disease recognition are remarkably improved.

The invention relates to the technical field of agricultural disease detection, and particularly discloses a ConvNeXt-GAN model generation method and system for blueberry leaf disease data set expansion. The method comprises the following steps: acquiring blueberry leaf disease images under different illumination conditions and weather environments in a blueberry cultivation base, preprocessing the acquired images, and further dividing to obtain a training set, a verification set and a test set; the StyleGAN3 generative adversarial network is improved; training the improved StyleGAN3 model by using the training set, continuously monitoring the performance of the model through the verification set, carrying out hyper-parameter tuning according to a monitoring result, and carrying out comprehensive evaluation on the performance of the model by using the test set to obtain an optimal improved StyleGAN3 model; and generating a blueberry leaf disease image by using the optimal improved StyleGAN3 model, thereby realizing construction of a high-quality blueberry leaf disease data set. The image generated by the method is accurate in scab reduction, high in semantic consistency, diversified in sample and low in redundancy, can be migrated to other plant disease scenes, and is wide in popularization prospect.

The invention provides a hyperspectrum-based citrus leaf lesion diagnosis system and a hyperspectrum-based citrus leaf lesion diagnosis method, and the hyperspectrum-based citrus leaf lesion diagnosis system and the hyperspectrum-based citrus leaf lesion diagnosis method disclosed by the invention have the advantages that on the basis of analyzing hyperspectral imaging data of citrus leaves without diseases, lack of nutrients, black spots and yellow shoot; three parameters of yellow wave band reflectivity, infrared wave band slope and inflection point wavelength are used as characteristic quantities, and classification of four types of blades is realized by applying a support vector machine (RBF-SVM) classification model based on a Gaussian radial basis kernel function. The method solves the problems that when citrus tree disease information is detected through a field detection method at present, long-time observation with eyes is needed, subjective judgment of observers is depended, and misjudgment is likely to be caused; according to the present invention, the problem that the citrus tree disease information detection by using the chemical detection method needs the special person to detect by using the professional equipment so as not to accurately and rapidly detect each production stage of the citrus tree can be solved, and the citrus leaf disease can be rapidly and accurately diagnosed.

The present application relates to the field of agricultural intelligent detection and computer vision, and specifically discloses a cherry tree leaf disease detection method based on a convolutional neural network. The method synchronously collects visible light images and hyperspectral transmission images of cherry tree leaves, representing external morphology and internal physiological state respectively; through a double-flow convolutional neural network, multi-scale spatial features and spectral features fused with vegetation index and optical physics model are extracted respectively, and cross-modal alignment and fusion are performed to generate a unified disease state representation vector; finally, the health state, disease type and risk level of the leaves are determined through a hierarchical classification structure. Through the above technical solution, the present application realizes early and advanced warning of cherry tree leaf diseases, improves detection sensitivity and robustness in the field environment, and provides decision support for precise pesticide application and green prevention and control.

The invention discloses a ginger stem and leaf disease and insect pest recognition optimization method based on image recognition, particularly relates to the technical field of image data processing, and is used for solving the problem that the long-term recognition accuracy is reduced due to the fact that an existing fixed model cannot adapt to slow drifting of field features. According to the method, the feature drift degree is quantified by monitoring the topological structure stability of a model feature space, and whether the model needs to be updated is judged accordingly; when updating is needed, a core feature area causing model confusion is positioned based on historical misjudgment data, new samples are directionally collected in a corresponding field area, and then model parameters are optimized; therefore, prospective evaluation and precise updating of the cognitive state of the model are realized, and long-term stability and high efficiency of the recognition system in a dynamic environment are guaranteed.

The invention discloses a plant leaf disease and insect pest detection method and system suitable for edge calculation, and relates to the technical field of plant leaf disease and insect pest detection. The method comprises the following steps: collecting and sorting plant leaf pest and disease damage and stressed leaf images, and constructing a basic image data set for subsequent model training and evaluation; preprocessing the basic image data set through data enhancement and standardization, and constructing an image for model training; constructing a Mama-based plant leaf disease and insect pest classification model; by optimizing an edge computing platform, lightweight design is carried out on the model; performing model training to obtain a pre-training model; and detecting plant leaf diseases and insect pests by using the trained plant leaf disease and insect pest classification model. The method can effectively reduce the complexity of the model and improve the detection speed.

The application discloses a tomato leaf disease identification method based on an improved DenseNet model, replaces a bottleneck layer of the original Densenet model with a Ghost bottleneck module, greatly reduces the calculation amount and the parameter amount of the model while guaranteeing the identification performance, replaces ordinary convolution with a convolution kernel size of 3*3 in the second Ghost module in the second Ghost bottleneck with a hollow convolution, and replaces ordinary convolution with a convolution kernel size of 3*3 in the second Ghost module in the third Ghost bottleneck with a hollow convolution, improves the identification precision of the tomato diseases with multi-scale changes, introduces a CMIFA attention mechanism module, improves the identification precision of the tomato diseases in a complex background while only increasing a small amount of parameters and calculation amount, and has the advantages of high identification precision, simple model structure, small parameter amount and calculation amount of the model, and can be popularized and used in actual life.

This invention relates to the field of image analysis technology, specifically to a method and system for leaf disease image analysis based on supervised contrastive learning. The method includes: acquiring multispectral image data of a leaf collected by a multispectral device and depth distance information of each pixel on the leaf image surface; constructing a leaf structure model of the leaf surface based on the multispectral image data and depth distance information; analyzing the structural features in the leaf structure model to obtain leaf feature data; determining leaf physiological state information based on the leaf feature data; and using the leaf physiological state information and a preset supervised contrastive learning model to perform disease feature analysis on the leaf, obtaining the leaf disease analysis results. The purpose of this invention is to solve the problem that existing technologies struggle to effectively distinguish between dust interference and disease features, leading to low accuracy in leaf disease detection.

AI-Enabled Crop Leaf Disease Identification Device

The invention relates to the technical field of crop image processing, in particular to a crop leaf disease and pest detection method and system, and the method comprises the steps: obtaining an initial crop leaf image, and improving an existing model and a parallel secondary division operation to obtain an improved YOLOv12n model, including: a multi-scale parallel secondary division operation, replacing C3k2 and A2f modules of a backbone network and a neck network in the existing model with an MD2f module, replacing all down-sampling structure blocks in the existing model with an SRCDown network, and performing iterative optimization training on the improved YOLOv12n model to obtain a crop leaf disease and insect pest detection model; and inputting the initial crop leaf image into a crop leaf pest detection model for detection, and outputting a leaf pest detection result. According to the method, on the premise that the accuracy is similar to that of a YOLOv12n model, the storage loss can be reduced, and the detection efficiency of crop leaf diseases and insect pests can be improved.

The utility model relates to the field of wheat disease observation, in particular to wheat leaf disease optical observation equipment, which adopts the technical scheme that the wheat leaf disease optical observation equipment comprises a leaf disease observer main body, a clamping scanner, a handheld rod, a display auxiliary screen, a connecting stud shaft and a rotating frame, the right end of the leaf disease observer body is fixedly connected with a connecting stud shaft, the front end and the rear end of the connecting stud shaft are provided with protruding blocks distributed around the connecting stud shaft at equal intervals, a rotating frame is rotationally installed on the connecting stud shaft, and the left end of the rotating frame is fixedly connected with an extending frame. According to the wheat leaf disease optical observation device, a larger display platform can be provided for information fed back by the wheat leaf disease optical observation device through the display auxiliary screen, so that a user can conveniently know the information of wheat leaf diseases, and the user can conveniently dial the display angle of the display auxiliary screen to perform rotation adjustment through the connecting stud shaft and the rotating frame which are rotationally connected; and the convex block and the groove can be used for limiting and fixing the display secondary screen which is rotationally adjusted.

The invention relates to a plant leaf disease image small sample classification method, and belongs to the technical field of plant leaf disease image identification and classification. The method comprises the following steps: inputting a preprocessed to-be-classified plant leaf image data set into an adaptive multi-scale feature extraction module HTEN to obtain a multi-scale feature vector; optimizing the preprocessed to-be-classified plant leaf image data set based on the multi-scale feature vector to obtain an optimized data set, and generating a category prototype vector based on the optimized data set; and obtaining intra-class compactness loss and inter-class orthogonality loss based on the class prototype vector by adopting a feature space constraint strategy, obtaining a total loss function in combination with standard prototype network classification loss, and realizing classification of disease images in the plant leaf image data set by minimizing the total loss function. The method aims at solving the technical problems that in the prior art, fine granularity difference capture is insufficient, the feature expression ability is limited, and classification precision is difficult to guarantee.

The application discloses a tomato leaf disease detection method based on an SSP-DETR model, which is realized through the SSP-DETR model and comprises the following steps: acquiring a tomato leaf disease image dataset and classifying; inputting the tomato leaf disease image into a StarNet network module for feature extraction and outputting feature maps P2, P3, P4 and P5 of different dimensions; inputting the P5 into an attention feature extraction module and outputting F5; inputting the P2, P3, P4 and F5 into a multi-scale fusion module for feature fusion; inputting the fused feature map into a Decoder decoder and outputting a tomato leaf disease area prediction frame; acquiring the tomato leaf disease area prediction frame and a disease area classification in the corresponding tomato leaf disease image, and optimizing the model by using a loss function for back propagation; and inputting the tomato leaf disease image into the optimized model to obtain a disease detection result. The application can realize comprehensive detection of tomato leaf diseases, has high detection precision and good robustness.

The application discloses a rice leaf disease identification method in an open scene, and relates to the technical field of computer vision, and specifically comprises the following steps: step 1: constructing an image enhancement set based on an adversarial attention mask; step 2: defining an entropy toughness index, and constructing a dynamic high-reliability sample library according to the index; step 3: realizing steady-state linear transformation through dynamic weight distribution; and step 4: combining the optimized pre-training classifier and the linear transformation matrix to form a final rice leaf disease identification model. Through the combination of the adversarial mask, the dynamic high-reliability sample library and the steady-state linear transformation, the application effectively solves the problems of poor robustness and difficult field adaptation of the model in an open farmland environment, and improves the performance of the rice leaf disease identification model in an open scene.

The invention discloses a comprehensive cultivation method for preventing and controlling diseases and insect pests of forsythia suspensa, and relates to the technical field of forsythia suspensa planting, and the method comprises the following steps: S1, selecting a land parcel meeting the growth requirements of forsythia suspensa, and completing deep ploughing, fertilization, furrowing and necessary soil disinfection; qualified cutting slips are selected, treated and then subjected to cuttage, and seedlings are cultivated through seedling management; seedlings obtained by seedling raising are transplanted and planted according to specified density in a proper period; performing water and fertilizer management and pruning management to maintain the robust growth state of plants; s2, preparing a composite immune resistance inducer; s3, spraying an immune resistance inducer in a programmed manner; s4, synergistically implementing physical prevention and control and biological prevention and control measures; according to the cultivation method, through scientific site selection and standardized basic cultivation, and programmed spraying of the complex immune resistance inducer in a specific ratio in a key phenological period, the pain points of pathogenic bacteria accumulation, easy waterlogging in summer and the like of continuous cropping plots can be solved in a targeted manner, the stress resistance of forsythia suspensa is effectively improved, leaf diseases and chewing pests are effectively prevented, and the yield is increased.

The present application relates to the technical field of computer vision target detection, and particularly relates to an apple leaf disease detection method based on an improved YOLOX-S algorithm, wherein all the cross-stage local networks of a YOLOX-S model backbone network and a feature fusion network are replaced by using a dynamic cross-stage local network structure of full-dimensional dynamic convolution, and then an adaptive feature fusion mode is introduced to fuse features of different feature scales on the basis of a feature pyramid structure PAFPN, and then a loss function is optimized according to actual data sets, and the optimized single-stage target detection model is trained, so that the trained model can be used to detect apple leaves with diseases, and obtain disease types and position information, the model proposed in the method improves the detection precision of the model, greatly reduces the additional parameters and the calculation amount of the model finally introduced, improves the problem of feature information loss of targets of different scales, and improves the average precision of the model.

The invention discloses a rice leaf disease recognition sampler, and belongs to the technical field of plant sampling. A material moving and supporting mechanism for placing a deep hole sampling box is mounted at the inner bottom of the base station, an anti-pollution automatic girdling and sampling mechanism is fixedly mounted at the upper end of the base station, and the protective cover covers the outer side of the anti-pollution automatic girdling and sampling mechanism; the anti-pollution automatic girdling sampling mechanism comprises a synchronous driving die changing assembly, an upper die mounting block, a lower die mounting block, a floating telescopic assembly, an upper die assembly, a lower die assembly, a first cleaning and disinfecting assembly and a second cleaning and disinfecting assembly. According to the invention, the surface characters of the rice leaves can be identified, the areas with disease symptoms of the rice leaves can be sampled and collected, the obtained samples can be automatically fed into different hole sites of the deep hole sampling box, a large number of rice leaf disease samples can be rapidly and efficiently collected, the operation is simple, and popularization and use are facilitated.