📅 02 October 2025
DOI: 10.62411/jcta.14620
EDANet: A Novel Architecture Combining Depthwise Separable Convolutions and Hybrid Attention for Efficient Tomato Disease Recognition
Journal of Computing Theories and Applications
Universitas Dian Nuswantoro
📄 Abstract
Tomato crop yields face significant threats from plant diseases, with existing deep learning solutions often computationally prohibitive for resource-constrained agricultural settings; to address this gap, we propose Efficient Disease Attention Network (EDANet), a novel lightweight architecture combining depthwise separable convolutions with hybrid attention mechanisms for efficient Tomato disease recognition. Our approach integrates channel and spatial attention within hierarchical blocks to prioritize symptomatic regions while utilizing depthwise decomposition to reduce parameters to only 104,043 (multiple times smaller than MobileNet and EfficientNet). Evaluated on ten tomato disease classes from PlantVillage, EDANet achieves 97.32% accuracy and exceptional (~1.00) micro-AUC, with perfect recognition of Mosaic virus (100% F1-score) and robust performance on challenging cases like Early blight (93.2% F1) and Target Spot (93.6% F1). The architecture processes 128×128 RGB images in ~23ms on standard CPUs, enabling real-time field diagnostics without GPU dependencies. This work bridges laboratory AI and practical farm deployment by optimizing the accuracy-efficiency tradeoff, providing farmers with an accessible tool for early disease intervention in resource-limited environments.
🔖 Keywords
#Attention mechanisms; Computer vision; Deep learning; Depthwise separable convolutions; Lightweight models; Plant disease classification; Precision agriculture; Tomato disease detection
ℹ️ Informasi Publikasi
Tanggal Publikasi
02 October 2025
Volume / Nomor / Tahun
Volume 3, Nomor 2, Tahun 2025
📝 HOW TO CITE
Ibrahim, Yusuf; O. Momoh, Muyideen; O. Shobowale, Kafayat; Mukhtar Abubakar, Zainab; Yahaya, Basira, "EDANet: A Novel Architecture Combining Depthwise Separable Convolutions and Hybrid Attention for Efficient Tomato Disease Recognition," Journal of Computing Theories and Applications, vol. 3, no. 2, Oct. 2025.
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