Yang Shuai, Xing Ziyao, Wang Hengbin, Dong Xinrui, Gao Xiang, Liu Zhe, Zhang Xiaodong, Li Shaoming, Zhao Yuanyuan
College of Land Science and Technology, China Agricultural University, Beijing 100083, China.
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China.
Insects. 2023 Mar 10;14(3):278. doi: 10.3390/insects14030278.
The frequent occurrence of crop pests and diseases is one of the important factors leading to the reduction of crop quality and yield. Since pests are characterized by high similarity and fast movement, this poses a challenge for artificial intelligence techniques to identify pests in a timely and accurate manner. Therefore, we propose a new high-precision and real-time method for maize pest detection, Maize-YOLO. The network is based on YOLOv7 with the insertion of the CSPResNeXt-50 module and VoVGSCSP module. It can improve network detection accuracy and detection speed while reducing the computational effort of the model. We evaluated the performance of Maize-YOLO in a typical large-scale pest dataset IP102. We trained and tested against those pest species that are more damaging to maize, including 4533 images and 13 classes. The experimental results show that our method outperforms the current state-of-the-art YOLO family of object detection algorithms and achieves suitable performance at 76.3% mAP and 77.3% recall. The method can provide accurate and real-time pest detection and identification for maize crops, enabling highly accurate end-to-end pest detection.
农作物病虫害频发是导致作物品质和产量下降的重要因素之一。由于害虫具有高度相似性且移动速度快，这给人工智能技术及时、准确地识别害虫带来了挑战。因此，我们提出了一种新的用于玉米害虫检测的高精度实时方法——Maize-YOLO。该网络基于YOLOv7，插入了CSPResNeXt-50模块和VoVGSCSP模块。它可以在降低模型计算量的同时提高网络检测精度和检测速度。我们在典型的大规模害虫数据集IP102中评估了Maize-YOLO的性能。我们针对对玉米危害较大的害虫种类进行训练和测试，包括4533张图像和13个类别。实验结果表明，我们的方法优于当前最先进的YOLO系列目标检测算法，在平均精度均值（mAP）为76.3%和召回率为77.3%时取得了合适的性能。该方法可为玉米作物提供准确、实时的害虫检测与识别，实现高精度的端到端害虫检测。
