@Article{jai.2025.072332,
AUTHOR = {Qiang Peng, Jia-Yu Yang, Xu-Yu Xiang},
TITLE = {A Lightweight and Optimized YOLO-Lite Model for <i>Camellia oleifera</i> Leaf Disease Recognition},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {7},
YEAR = {2025},
NUMBER = {1},
PAGES = {437--450},
URL = {http://www.techscience.com/jai/v7n1/64051},
ISSN = {2579-003X},
ABSTRACT = {<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>.},
DOI = {10.32604/jai.2025.072332}
}