TL;DR
DFruitNet is proposed, a lightweight dual-attention-based convolutional neural network for dragon fruit disease classification and severity estimation that integrates channel and spatial attention mechanisms with multiscale feature fusion to effectively capture both local and global visual patterns.
The prompt and precise identification of plant diseases is a critical challenge in contemporary agriculture, as any delay in identification may lead to substantial reductions in crop yields and financial repercussions. Recent developments in deep learning (DL) have facilitated automated plant leaf image disease detecti...
The experimental results demonstrate that AFS-PLDCNet achieved superior classification accuracy compared to existing single-backbone CNNs and is well-suited for real-time, field-level leaf disease classification and precision agriculture systems.
This work proposes EfficientNet-CBAM-Prototype (ECP-Net), a unique end-to-end deep learning architecture that achieves 98.6% test accuracy, 98.59% F1-score, and 98.65% precision on the PlantVillage tomato subset, and proposes a two-phase training strategy wherein CBAM is frozen during phase 1 to allow prototype stabili...
Precision agriculture increasingly relies on automated crop monitoring to support disease prevention and harvest decisions. However, most existing systems treat fruit type, ripeness, and disease classification as separate tasks, leading to high computational costs in real-world deployments. This paper proposes an effic...
A deep learning model designed to automatically detect grape leaf diseases based on images, using a pretrained ResNet50 which is trained on ImageNet as feature extractor and a Convolutional Block Attention Module to boost its discriminative capacity is introduced.
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