aceComprehensive plant disease classification and severity estimation for sustainable farming via automatic segmentation and multi-scale feature fusion
Detecting plant leaf diseases at an early stage is one of the most important requirements for sustainable agriculture, increasing crop productivity, and achieving the global Sustainable Development Goals (SDGs). However, accurately recognizing them in real-world farm fields can still be difficult due to factors such as background complexity, changes in light conditions, and very similar looking classes from a visual standpoint. In order to solve these problems, the authors here present a new Multi-Scale Feature…
Multi-Scale Feature Fusion model combining U-Net segmentation, EfficientNet and attention autoencoder features fused via CCA and YOLO classification achieved 99.95% accuracy on apple leaf disease datasets, enabling early detection for sustainable agriculture.
Evidence
- Peer-reviewedBMC Plant Biology2026-07-18
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Truvace Impact Record TRV-2026-0262, v1: “Comprehensive plant disease classification and severity estimation for sustainable farming via automatic segmentation and multi-scale feature fusion.” Truvace, 2026-07-19. /record/TRV-2026-0262 (accessed at citation time). sha256 20184feae7cb7992…
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