Wang Xuewei, Liu Jun
Shandong Provincial University Laboratory for Protected Horticulture, Blockchain Laboratory of Agricultural Vegetables, Weifang University of Science and Technology, Weifang, China.
Front Plant Sci. 2021 May 11;12:620273. doi: 10.3389/fpls.2021.620273. eCollection 2021.
Plant disease detection technology is an important part of the intelligent agricultural Internet of Things monitoring system. The real natural environment requires the plant disease detection system to have extremely high real time detection and accuracy. The lightweight network MobileNetv2-YOLOv3 model can meet the real-time detection, but the accuracy is not enough to meet the actual needs. This study proposed a multiscale parallel algorithm MP-YOLOv3 based on the MobileNetv2-YOLOv3 model. The proposed method put forward a multiscale feature fusion method, and an efficient channel attention mechanism was introduced into the detection layer of the network to achieve feature enhancement. The parallel detection algorithm was used to effectively improve the detection performance of multiscale tomato gray mold lesions while ensuring the real-time performance of the algorithm. The experimental results show that the proposed algorithm can accurately and real-time detect multiscale tomato gray mold lesions in a real natural environment. The F1 score and the average precision reached 95.6 and 93.4% on the self-built tomato gray mold detection dataset. The model size was only 16.9 MB, and the detection time of each image was 0.022 s.
植物病害检测技术是智能农业物联网监测系统的重要组成部分。真实的自然环境要求植物病害检测系统具有极高的实时检测能力和准确性。轻量级网络MobileNetv2-YOLOv3模型能够满足实时检测需求，但准确性不足以满足实际需要。本研究基于MobileNetv2-YOLOv3模型提出了一种多尺度并行算法MP-YOLOv3。该方法提出了一种多尺度特征融合方法，并在网络检测层引入了高效的通道注意力机制以实现特征增强。并行检测算法在保证算法实时性的同时有效提高了多尺度番茄灰霉病病斑的检测性能。实验结果表明，所提算法能够在真实自然环境中准确、实时地检测多尺度番茄灰霉病病斑。在自建的番茄灰霉病检测数据集上，F1分数和平均精度分别达到了95.6%和93.4%。模型大小仅为16.9 MB，每张图像的检测时间为0.022 s。
