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Detection of Apple Leaf Diseases Based on LightYOLO-AppleLeafDx

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Veröffentlicht in:Plants vol. 14, no. 4 (2025), p. 599
1. Verfasser: Zou, Hongyan
Weitere Verfasser: Lv, Peng, Zhao, Maocheng
Veröffentlicht:
MDPI AG
Schlagworte:
China
Crop yield
Accuracy
Datasets
Plant diseases
Frames (data processing)
Apples
Disease
Precision agriculture
Frames per second
Neural networks
Ablation
Fruits
Modules
Leaves
Real time
Disease detection
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Abstract:Early detection of apple leaf diseases is essential for enhancing orchard management efficiency and crop yield. This study introduces LightYOLO-AppleLeafDx, a lightweight detection framework based on an improved YOLOv8 model. Key enhancements include the incorporation of Slim-Neck, SPD-Conv, and SAHead modules, which optimize the model’s structure to improve detection accuracy and recall while significantly reducing the number of parameters and computational complexity. Ablation studies validate the positive impact of these modules on model performance. The final LightYOLO-AppleLeafDx achieves a precision of 0.930, mAP@0.5 of 0.965, and mAP@0.5:0.95 of 0.587, surpassing the original YOLOv8n and other benchmark models. The model is highly lightweight, with a size of only 5.2 MB, and supports real-time detection at 107.2 frames per second. When deployed on an RV1103 hardware platform via an NPU-compatible framework, it maintains a detection speed of 14.8 frames per second, demonstrating practical applicability. These results highlight the potential of LightYOLO-AppleLeafDx as an efficient and lightweight solution for precision agriculture, addressing the need for accurate and real-time apple leaf disease detection.
ISSN:2223-7747
DOI:10.3390/plants14040599
Quelle:Agriculture Science Database