Detection of Apple Leaf Diseases Based on LightYOLO-AppleLeafDx
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| Publicado en: | Plants vol. 14, no. 4 (2025), p. 599 |
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| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3171182026 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2223-7747 | ||
| 024 | 7 | |a 10.3390/plants14040599 |2 doi | |
| 035 | |a 3171182026 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231551 |2 nlm | ||
| 100 | 1 | |a Zou, Hongyan | |
| 245 | 1 | |a Detection of Apple Leaf Diseases Based on LightYOLO-AppleLeafDx | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a China | |
| 653 | |a Crop yield | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Plant diseases | ||
| 653 | |a Frames (data processing) | ||
| 653 | |a Apples | ||
| 653 | |a Disease | ||
| 653 | |a Precision agriculture | ||
| 653 | |a Frames per second | ||
| 653 | |a Neural networks | ||
| 653 | |a Ablation | ||
| 653 | |a Fruits | ||
| 653 | |a Modules | ||
| 653 | |a Leaves | ||
| 653 | |a Real time | ||
| 653 | |a Disease detection | ||
| 700 | 1 | |a Lv, Peng | |
| 700 | 1 | |a Zhao, Maocheng | |
| 773 | 0 | |t Plants |g vol. 14, no. 4 (2025), p. 599 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3171182026/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3171182026/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3171182026/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |