Zheng Yong, Zheng Weiheng, Du Xia
Xiamen University of Technology, Fujian, 361024, China.
Hunan Provincial Key Laboratory of Remote Sensing Monitoring for Eco-environment in Dongting Lake Area, Changsha, 410004, Hunan, China.
Sci Rep. 2024 Dec 2;14(1):29888. doi: 10.1038/s41598-024-81587-5.
Timely and accurate detection of rice pests is highly important for pest control, as well as for improving rice yield and quality. However, owing to the high interclass similarity, significant intraclass age differences, and complex backgrounds among different pests, accurately and rapidly identifying a variety of rice pests via deep neural network models poses a significant challenge. To address this issue, this paper presents a fast and accurate method for rice pest detection and identification named Rice-YOLO (You Only Look Once). This model is based on YOLOv8-N and incorporates an efficient detection head designed for the complex characteristics of pests. Additionally, deep supervision layers were introduced into the network, along with the incorporation and improvement of the dynamic upsampling module. The experimental data included the large-scale pest public dataset IP102 and the sixteen-class rice pest dataset R2000. The experimental results demonstrated that Rice-YOLO outperformed previous object detection algorithms, with 78.1% mAP@0.5, 62.9% mAP@0.5:0.95, and 74.3% F1 scores.
及时准确地检测水稻害虫对于害虫防治以及提高水稻产量和质量至关重要。然而，由于不同害虫之间类间相似度高、类内年龄差异显著以及背景复杂，通过深度神经网络模型准确快速地识别多种水稻害虫面临重大挑战。为解决这一问题，本文提出了一种名为Rice-YOLO（You Only Look Once）的快速准确的水稻害虫检测与识别方法。该模型基于YOLOv8-N，并结合了针对害虫复杂特征设计的高效检测头。此外，在网络中引入了深度监督层，并对动态上采样模块进行了融合与改进。实验数据包括大规模害虫公共数据集IP102和十六类水稻害虫数据集R2000。实验结果表明，Rice-YOLO优于先前的目标检测算法，平均精度均值（mAP）@0.5为78.1%，mAP@0.5:0.95为62.9%，F1分数为74.3%。
