Maize-YOLO: A New High-Precision and Real-Time Method for Maize Pest Detection

Simple Summary Maize is one of the world's most important crops, and pests can seriously damage its yield and quality. Detection of maize pests is vital to ensuring the excellent productivity of maize. Traditional methods of pest detection are generally complex and inefficient. In recent years, there have been many cases of plant pest detection through deep learning. In this paper, we propose a new real-time pest detection method based on deep convolutional neural networks (CNN), which not only offers higher accuracy but also faster efficiency and less computational effort. Experimental results on a maize pest dataset show that the proposed method outperforms other methods and that it can balance well between accuracy, efficiency, and computational effort. 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.