ArticleFrontiers in plant science2020
A Deep-Learning-Based Real-Time Detector for Grape Leaf Diseases Using Improved Convolutional Neural Networks.
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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41 citing papers in PubMed, 285 citations in OpenAlex.
- A Large-Scale In-the-wild Dataset for Plant Disease Segmentation.Scientific data · 2026Article
- A Survey of Crop Disease Recognition Methods Based on Spectral and RGB Images.Journal of imaging · 2026Review
- Detection method for munage grape clusters and abnormal berries under color-similar backgrounds.Frontiers in plant science · 2026Article
- An attention-augmented lightweight convolutional framework for fine-grained plant leaf disease classification.Frontiers in plant science · 2026Article
- Deep learning technique for plant disease classification and pest detection and model explainability elevating agricultural sustainability.BMC plant biology · 2025Article
- Recent advances in plant disease detection: challenges and opportunities.Plant methods · 2025Review
- Grapes leaf disease dataset for precision agriculture.Data in brief · 2025Article
- Integrating CBAM and Squeeze-and-Excitation Networks for Accurate Grapevine Leaf Disease Diagnosis.Food science & nutrition · 2025Article
- BED-YOLO: An Enhanced YOLOv10n-Based Tomato Leaf Disease Detection Algorithm.Sensors (Basel, Switzerland) · 2025Article
- VitiForge: a new procedural pipeline approach for grapevine disease identification under data scarcity.Frontiers in plant science · 2025Article
- An application of YOLOv8 integrated with attention mechanisms for detection of grape leaf black rot spots.PloS one · 2025Article
- TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection.Frontiers in plant science · 2025Article
- GhostConv+CA-YOLOv8n: a lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds.Frontiers in plant science · 2025Article
- Artificial Intelligence Techniques in Grapevine Research: A Comparative Study with an Extensive Review of Datasets, Diseases, and Techniques Evaluation.Sensors (Basel, Switzerland) · 2024Review
- Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM.Scientific reports · 2024Article
- Enhanced deep learning technique for sugarcane leaf disease classification and mobile application integration.Heliyon · 2024Article
- A deep multi-task learning approach to identifying mummy berry infection sites, the disease stage, and severity.Frontiers in plant science · 2024Article
- Real-time and lightweight detection of grape diseases based on Fusion Transformer YOLO.Frontiers in plant science · 2024Article
- SerpensGate-YOLOv8: an enhanced YOLOv8 model for accurate plant disease detection.Frontiers in plant science · 2024Article
- Stacking-based and improved convolutional neural network: a new approach in rice leaf disease identification.Frontiers in plant science · 2023Article
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Authors and funding
6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Black rot, Black measles, Leaf blight and Mites of grape are four common grape leaf diseases that seriously affect grape yield. However, the existing research lacks a real-time detecting method for grape leaf diseases, which cannot guarantee the healthy growth of grape plants. In this article, a real-time detector for grape leaf diseases based on improved deep convolutional neural networks is proposed. This article first expands the grape leaf disease images through digital image processing technology, constructing the grape leaf disease dataset (GLDD). Based on GLDD and the Faster R-CNN detection algorithm, a deep-learning-based Faster DR-IACNN model with higher feature extraction capability is presented for detecting grape leaf diseases by introducing the Inception-v1 module, Inception-ResNet-v2 module and SE-blocks. The experimental results show that the detection model Faster DR-IACNN achieves a precision of 81.1% mAP on GLDD, and the detection speed reaches 15.01 FPS. This research indicates that the real-time detector Faster DR-IACNN based on deep learning provides a feasible solution for the diagnosis of grape leaf diseases and provides guidance for the detection of other plant diseases.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.