Article

Lightweight Visual Detection Framework for Complex Background Grape Leaf Disease Identification

2026-06-08

Abstract excerpt

<title>Abstract</title> <p>Accurate crop disease detection supports precision agriculture, but field-deployable identification remains hindered by complex backgrounds, varying illumination, and heavy deep learning models. This work presents a lightweight visual detection approach for grape leaf diseases under unconstrained field conditions. Built on the YOLO11n backbone, the method integrates three customized mod...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 4c500e86-88ad-5551-b77c-88a662139611
DOI: 10.21203/rs.3.rs-9554582/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Lightweight Visual Detection Framework for Complex Background Grape Leaf Disease IdentificationDOI 10.21203/rs.3.rs-9554582/v1Read

REA-CNN: A Region-Aware & Enhanced Attention CNN for Robust Leaf Disease ClassificationSemantic proximity Read

Lightweight Real-Time Detection Transformer for Tomato Leaf Disease Recognition in Complex Agricultural ScenariosSemantic proximity Read

B2-GraftingNet: A Hybrid Deep-Machine Learning Framework with Explainable AI for Automated Grape Leaf Disease DetectionSemantic proximity Read

A Novel Multi Class Real World Fruit and Leaf Disease Image Dataset for Crop Health AnalysisSemantic proximity Read

Plant Leaf Disease Detection Using Deep Learning: A Multi-Dataset ApproachSemantic proximity Read

A Comprehensive Image Dataset of Fruit and Leaf Diseases Across Six Horticultural Crops for Deep Learning ApplicationsSemantic proximity Read

Towards high throughput in-field detection and quantification of wheat foliar diseases with deep learningSemantic proximity Read

A lightweight convolutional and vision transformer hybrid network for parameter efficient plant disease classificationSemantic proximity Read

Mamba-YOLO: A Hybrid Architecture with Linear-Complexity Selective Scan Mechanisms for Enhanced Microscopic Rice Disease DetectionSemantic proximity Read

Unsupervised Anomaly Detection in Plant Disease LocalizationSemantic proximity Read

Attention U-Net for Unsupervised Anomaly Detection in Plant Disease LocalizationSemantic proximity Read

YOLOv11-RCDWD: A New Efficient Model for Detecting Maize Leaf Diseases Based on the Improved YOLOv11Semantic proximity Read