TY  - EJOU
AU  - Peng, Qiang 
AU  - Yang, Jia-Yu 
AU  - Xiang, Xu-Yu 

TI  - A Lightweight and Optimized YOLO-Lite Model for <i>Camellia oleifera</i> Leaf Disease Recognition
T2  - Journal on Artificial Intelligence

PY  - 2025
VL  - 7
IS  - 1
SN  - 2579-003X

AB  - <i>Camellia oleifera</i> is one of the four largest oil tree species in the world, and also an important economic crop in China, which has overwhelming economic benefits. However, <i>Camellia oleifera</i> is invaded by various diseases during its growth process, which leads to yield reduction and profit damage. To address this problem and ensure the healthy growth of <i>Camellia oleifera</i>, the purpose of this study is to apply the lightweight network to the identification and detection of <i>camellia oleifolia</i> leaf disease. The attention mechanism was combined for highlighting the local features and improve the attention of the model to the key areas of <i>Camellia oleifera</i> disease images. To prove the recognition of the optimized network on <i>Camellia oleifera</i> leaf disease, we tested the network performance of the optimized model with other object detection algorithms such as YOLOV5s, SSD, Faster-RCNN, YOLOv8, and YOLOv10. The results show that the mAP, recall, and accuracy of the trained network achieved 82.9%, 75.7% and 80.6%, respectively. The optimized YOLO-Lite model has the advantages of small size and few parameters while ensuring high accuracy, thus it has a satisfactory effect on leaf disease identification of <i>Camellia oleifera</i>.
KW  - <i>Camellia oleifera</i> leaf identification; deep learning; object detection; optimized YOLO-Lite

DO  - 10.32604/jai.2025.072332