190 results about "Leaf disease" patented technology

The invention relates to the technical field of computer vision, in particular to a plant leaf disease severity assessment method based on position-guided focus characterization learning, which comprises the following steps: acquiring a disease leaf image; the disease leaf image is input into a hierarchical lesion fusion evaluation network model, a leaf disease severity evaluation result is obtained, the hierarchical lesion fusion evaluation network model is obtained based on training of a real training set, the real training set is a leaf image marked with the disease severity, and the leaf disease severity evaluation result is obtained; the hierarchical lesion fusion evaluation network model comprises a feature encoder with priori knowledge, a hierarchical lesion feature fusion module and an evaluation module, the feature encoder with priori knowledge is obtained by inputting an image pair into a position-guided lesion characterization learning network for training, the image pair refers to health and disease states of the same leaf, and the evaluation module refers to the position-guided lesion characterization learning network. The leaf disease state is a lesion leaf generated from a healthy leaf through a diffusion model. According to the invention, more accurate positioning and severity evaluation can be carried out on the focus area.

The invention discloses a soybean leaf disease identification method based on multi-scale feature fusion Transform, and the method comprises the following steps: S1, obtaining different types of soybean leaf disease images from a public data set, S2, carrying out the preprocessing operation of the collected soybean leaf disease images, including rotation, overturning, brightness adjustment and contrast adjustment; s3, constructing a soybean leaf disease identification model; s4, verifying the trained model; s5, obtaining a to-be-identified soybean leaf disease image; s6, inputting a to-be-identified soybean leaf image into the constructed soybean leaf disease identification model, and identifying and outputting a disease type through the classifier; a pyramid pooling module and a self-attention mechanism are introduced, and a channel attention module is optimized, so that a global spatial dependency relationship among different scales can be obtained, and the recognition accuracy of the model is effectively improved.

The invention discloses a tomato leaf disease detection method based on an SSP-DETR model, and the method is realized through the SSP-DETR model, and comprises the steps: obtaining a tomato leaf disease image data set, and carrying out the classification; 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 P5 into an attention feature extraction module, and outputting F5; p2, P3, P4 and F5 are input 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 a tomato leaf disease area prediction frame and disease area classification in the corresponding tomato leaf disease image, and performing back propagation on the optimization model by using a loss function; and inputting the tomato leaf disease image into the optimized model to obtain a disease detection result. According to the method, the tomato leaf diseases can be comprehensively detected, the detection precision is high, and the robustness is good.

The invention discloses an apple leaf disease classification method and device, a terminal, a storage medium and a computer program product, and the method comprises the steps: collecting image data of apple leaves as a sample, and carrying out the preprocessing of the image data to obtain a training set and a test set; the method comprises the following steps: introducing an EnhancedCBAM attention mechanism, and constructing an improved ConvNeXt network as a new ConvNeXt network; training by using the training set to obtain a trained apple leaf disease classification model; testing by using the test set to obtain a corrected apple leaf disease classification model; acquiring image data of apple leaves to be detected; and detecting by using the corrected apple leaf disease classification model to obtain the disease type of the to-be-detected apple leaf. According to the scheme, the apple leaf disease classification model is obtained based on the improved ConvNeXt training to identify the to-be-detected apple leaf picture, the apple leaf disease can be rapidly identified, and the accuracy is good.

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 discloses a crop leaf disease diagnosis method and system driven by a large language model. Comprising the following steps: firstly, extracting a leaf disease image of a target crop and corresponding text semantic information from a basic crop management file so as to obtain an initial crop disease knowledge database; then, the initial crop disease knowledge database is optimized and updated in combination with visual understanding of a large language model; and then, constructing a crop disease classification agent model, inputting a to-be-diagnosed leaf disease image into the crop disease classification agent model, performing crop disease diagnosis by the crop disease classification agent model according to the crop disease knowledge database, and outputting a disease identification result. Under the condition that large-scale image training is not carried out, disease diagnosis can be accurately carried out on the crop disease images, disease image diagnosis requirements of different crops can be met, and the problem that existing work is insufficient in potential in the aspects of zero sample application and expandability is solved.

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 belongs to the field of crop germplasm resource identification and evaluation and genetic breeding, and particularly discloses a field leaf disease resistance identification system and method for large-scale sugarcane germplasm. Carrying out susceptibility grade division on a sugarcane sample to be detected; according to the susceptibility level of the to-be-detected sugarcane sample, obtaining the disease index of the sugarcane plant; and according to the disease index and a preset resistance evaluation system, obtaining the disease resistance level and the comprehensive disease resistance level of the sugarcane leaves. The method can quickly complete field disease resistance identification of leaf diseases of a large batch of sugarcane germplasm resources. Most importantly, the evaluation of the disease resistance of the sugarcane leaves integrates various leaf diseases, can reflect the comprehensive disease resistance of the sugarcane infected by the various leaf diseases, and better meets the actual requirements of breeders in sugarcane breeding.

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 relates to a wheat leaf disease monitoring method based on the Internet of Things. The method comprises the following steps: acquiring a wheat leaf image by using an Internet of Things camera; transmitting the wheat leaf images to an upper computer, and setting a corresponding disease type label for each wheat leaf image in the upper computer; performing illumination balance compensation on each wheat leaf image to generate a compensated wheat leaf image; inputting the compensated wheat leaf image as a sample into a neural network for training to obtain a wheat disease monitoring model; and inputting the target wheat leaf image into the wheat disease monitoring model to obtain the disease type of the corresponding wheat. According to the invention, the wheat leaf image is automatically collected by using the Internet of Things camera, and the wheat leaf image is input into the wheat disease monitoring model to complete disease identification, so that the dependence on manual inspection is reduced, the labor cost is reduced, and the wheat disease monitoring precision is improved.

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 crop disease quantification method based on an unmanned aerial vehicle image, and belongs to the technical field of crop disease identification, and the method comprises the steps: obtaining a leaf image through an unmanned aerial vehicle, and carrying out the preprocessing, and obtaining a leaf image data set; constructing a crop scab segmentation model based on UNet improvement, and performing training and evaluation optimization by using the leaf image data set to obtain an optimal crop scab segmentation model; and processing the unmanned aerial vehicle leaf image by using the optimal crop scab segmentation model to obtain a scab region, and calculating to obtain an area proportion of the scab region to complete quantification of the leaf disease degree. According to the method, UNet is improved through the feature extraction layer, the snake-shaped convolution layer and the cross attention layer, tiny and irregular disease spots in the unmanned aerial vehicle leaf image can be effectively segmented, the accuracy of disease spot region segmentation and the disease spot detection efficiency are improved, personal errors are reduced, and more scientific and accurate data support is provided for agricultural management.

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 provides a plant leaf disease image segmentation method based on boundary perception guidance. The method comprises the following steps: S1, obtaining a plant leaf sample image and a label image; s2, constructing an image segmentation model based on boundary perception guidance, wherein the image segmentation model comprises an encoder, a multi-scale cross fusion decoder and a boundary perception guidance unit; the encoder inputs a sample image and extracts features of different dimensions of the sample image, the output end of the encoder is connected to the multi-scale cross fusion decoder for feature processing, the output features of the multi-scale cross fusion decoder are input to the boundary perception guide unit, and the boundary perception guide unit outputs a segmentation result; s3, inputting the sample image and the label image into an image segmentation model to train the image segmentation model; and S4, obtaining a to-be-identified plant leaf image, and inputting the to-be-identified plant leaf image into the trained image segmentation model to obtain a disease part segmentation result of the to-be-identified plant leaf image.

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 discloses an apple leaf disease classification method based on a regional perception and lightweight hybrid Transform network. The method comprises the following steps: firstly, inhibiting interference of a low-frequency background and dynamic illumination by using a region extraction and fine-grained convolutional coding module, and adaptively sensing a leaf region and a disease region; secondly, a lightweight Transform block of a parallel cascade structure is designed to extract multi-scale texture and edge features, and adaptive selection of an optimal feature scale, mining of a long-distance dependency relationship and cross-scale information interaction and fusion are realized in a jump connection mode; then, extracting rich texture detail features of the apple leaf region by adopting feature weighting based on adaptive learning, an inverse residual mechanism and the attention degree of a channel attention enhancement network on region feature information; and finally, the generalization ability of the network is improved by jointly adopting a weighted cross entropy loss function and a focus loss function. Experimental results verify the effectiveness of the method.

The invention discloses a portable corn rust disease detection device and method, and relates to the field of corn rust disease detection. The image acquisition module acquires information data of target corn leaves; the main control module performs corn rust disease detection according to the spectral information based on a corn rust detection model, and determines a pixel point detection result; the pixel point detection result is a healthy point, an early disease point or a disease point; the corn rust detection model adopts a support vector machine classification model; performing pseudo-color enhancement processing on the image information by adopting a pseudo-color technology to obtain processed image information; segmenting the processed image information based on a YOLOv5 segmentation model, and determining a target leaf disease rate according to the segmented image information; and determining an overall leaf detection result corresponding to the corn rust disease according to the target leaf disease rate. The method is easy and convenient to operate, and the detection efficiency of the corn rust disease can be improved.

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 discloses a method for detecting rice leaf disease scabs based on computing power of a mobile device, detection of rice diseases is completed through an improved detection model based on YOLOv5, a ternary attention mechanism is introduced into the detection model to enhance attention feature extraction ability in YOLOv5, a C3CBAM module is used for replacing a C3 module of a neck network in YOLOv5, and the C3CBAM module is used for detecting the disease scabs of the rice leaf disease. And a lightweight convolution GhostConv is used to replace a convolution Conv in a backbone network in the YOLOv5, the lightweight convolution GhostConv is deployed to a mobile terminal Raspberry Pi and mnn acceleration is carried out, an input rice leaf image is detected, and a detection result is obtained. According to the method, the detection accuracy of the common rice leaf diseases is kept, and meanwhile, the relatively high reasoning speed can be kept on the Raspberry Pi of the mobile terminal.

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 crop leaf disease quantification method based on deep learning, belongs to the technical field of leaf disease detection, and solves the problems that time and labor are wasted and errors are large when crop leaf disease degree is manually observed and measured in the prior art. According to the method, the RGB image and the depth map are simultaneously input through the RDA U-Net model, so that interference of other leaves is eliminated, the leaf closest to the lens is conveniently found, the disease degree of the crop leaves is automatically quantified by using the deep learning technology, the efficiency and accuracy of disease degree detection are improved, subjective errors of manual observation and measurement are reduced, and the detection accuracy is improved. The method is suitable for large-scale farmland environment.

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 plant leaf disease and insect pest detection method and system, and relates to the field of intelligent agriculture, and the method comprises the following steps: calibrating a binocular vision system; collecting plant leaf disease and insect pest images by adopting a calibrated binocular vision system; using improved YOLOv8-seg to segment the disease and insect pest area of the image to obtain a left mask, a right mask and an integral plant leaf; calculating an overlapping region of the left mask and the right mask as a final pest and disease damage region; calculating the percentage of the final pest and disease damage area in the whole plant leaf as the plant leaf pest and disease damage degree; respectively acquiring geometric center coordinates of the left mask and the right mask; performing three-dimensional correction on the left mask and the right mask; and calculating three-dimensional coordinates corresponding to the left mask and the right mask after three-dimensional correction. The invention provides a plant leaf disease and insect pest detection method and system, and provides convenience for intelligent agricultural disease and insect pest diagnosis and agricultural mechanization operation.

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.