An Efficient Attention-Enhanced MobileNetV2 Framework for Plant Disease Detection on Resource-Constrained Devices
Abstract
Leaf disease diagnosis needs models that are accurate enough for agronomic use yet small enough for constrained computing settings. This study examines a late-attention MobileNetV2 design in which one Convolutional Block Attention Module (CBAM) is inserted between the last MobileNetV2 convolutional map and global average pooling. The experiments use 54,306 controlled-background PlantVillage images spanning 38 classes. Under a uniform saved-model re-evaluation, MobileNetV2 + CBAM obtained 97.17% accuracy and 97.15% weighted F1-score, whereas MobileNetV2 obtained 96.78% and 96.73%. On the converted models, paired testing gave a 0.64-percentage-point accuracy advantage for the CBAM variant (95% CI: 0.31–0.96; exact McNemar p
Fields of Study & Subjects
Smart Agriculture and AI
Advanced Neural Network Applications
Plant Disease Management Techniques
Publication details
- Journal / source
- Electronics
- Publisher
- MDPI AG
- Volume
- 15
- Issue
- 15
- Pages
- 3343–3343
- Publication date
- 28 July 2026
- Language
- EN