ArticleScientific reports2024
Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Attention-driven YOLOv11n-DeiT model for enhanced detection of tomato leaf diseases.Scientific reports · 2026Article
- Lightweight Cross-Domain Few-Shot Plant Disease Recognition Through Target-Domain Statistical Calibration.Sensors (Basel, Switzerland) · 2026Article
- Enhanced corn leaf disease detection using sharpness-aware minimization optimized CNNs.Plant methods · 2026Article
- ConvDeiT-Tiny: Adding Local Inductive Bias to DeiT-Ti for Enhanced Maize Leaf Disease Classification.Plants (Basel, Switzerland) · 2026Article
- A comprehensive evaluation of lightweight deep learning models for tomato disease classification on edge computing environments.Scientific reports · 2026Article
- A unified EfficientSwinB-based framework for medicinal plant recognition and safflower germplasm classification.Scientific reports · 2026Article
- LeafFusionNet: a hybrid deep learning approach for robust plant disease detection.Frontiers in artificial intelligence · 2026Article
- An integrated edge AI prototype for smart agriculture: real-time pest detection, physical trapping, and multi-node deployment analysis under field uncertainty.Frontiers in plant science · 2026Article
- Deep convolutional neural network-based automated identification and classification of mungbean foliar diseases.Frontiers in artificial intelligence · 2026Article
- Towards practical AI for agriculture: A self-supervised attention framework for Spinach leaf disease detection.PloS one · 2026Article
- Adaptive multi-scale feature refinement for wheat phenology recognition using cross-scale attention mechanisms.Frontiers in plant science · 2026Article
- High-performance parallel multi-scale attention network with explainable AI for intelligent diagnosis of leaf diseases in agricultural systems.Scientific reports · 2025Article
- Recent advances in plant disease detection: challenges and opportunities.Plant methods · 2025Review
- DBA-ViNet: an effective deep learning framework for fruit disease detection and classification using explainable AI.BMC plant biology · 2025Article
- AI and IoT-powered edge device optimized for crop pest and disease detection.Scientific reports · 2025Article
- Wheat Cultivation Suitability Evaluation with Stripe Rust Disease: An Agricultural Group Consensus Framework Based on Artificial-Intelligence-Generated Content and Optimization-Driven Overlapping Community Detection.Plants (Basel, Switzerland) · 2025Article
- Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety.Foods (Basel, Switzerland) · 2025Review
- A high-throughput ResNet CNN approach for automated grapevine leaf hair quantification.Scientific reports · 2025Article
- Real-time jute leaf disease classification using an explainable lightweight CNN via a supervised and semi-supervised self-training approach.Frontiers in plant science · 2025Article
- MobileNet-GDR: a lightweight algorithm for grape leaf disease identification based on improved MobileNetV4-small.Frontiers in plant science · 2025Article
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6 authors.
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Abstract
Crop diseases can significantly affect various aspects of crop cultivation, including crop yield, quality, production costs, and crop loss. The utilization of modern technologies such as image analysis via machine learning techniques enables early and precise detection of crop diseases, hence empowering farmers to effectively manage and avoid the occurrence of crop diseases. The proposed methodology involves the use of modified MobileNetV3Large model deployed on edge device for real-time monitoring of grape leaf disease while reducing computational memory demands and ensuring satisfactory classification performance. To enhance applicability of MobileNetV3Large, custom layers consisting of two dense layers were added, each followed by a dropout layer, helped mitigate overfitting and ensured that the model remains efficient. Comparisons among other models showed that the proposed model outperformed those with an average train and test accuracy of 99.66% and 99.42%, with a precision, recall, and F1 score of approximately 99.42%. The model was deployed on an edge device (Nvidia Jetson Nano) using a custom developed GUI app and predicted from both saved and real-time data with high confidence values. Grad-CAM visualization was used to identify and represent image areas that affect the convolutional neural network (CNN) classification decision-making process with high accuracy. This research contributes to the development of plant disease classification technologies for edge devices, which have the potential to enhance the ability of autonomous farming for farmers, agronomists, and researchers to monitor and mitigate plant diseases efficiently and effectively, with a positive impact on global food security.
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