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

Abstract

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.

Larval competition analysis and its effect on growth of Ostrinia furnacalis (Lepidoptera: Crambidae) at natural conditions in Northeast China

“…The use of YOLOv7 detection and tracking system enables tracking single larvae accurately and in real-time, allowing for in-depth analysis of their interactions and actions in competitive situations. Findings into competition for resources, territorial behavior, and aggressiveness among pest larvae have been gained through this approach (Yang et al 2023, Yuan 2023). A detailed examination of both intra- and interspecific competition dynamics has been possible with YOLOv7 to identify differences between the larvae of Asian corn borer and other species.…”

Artificial neuronal networks are revolutionizing entomological research

“…The training process for AI involves feeding them a large number of images of different pests and allowing them to learn the patterns and features that distinguish them from one another. This is typically done using CNNs capable of learning complex patterns and features from visual data (Hassan et al., 2023; Jackulin & Murugavalli, 2022; Kuzuhara et al., 2020; Yang et al., 2023; Zhao, Liu, et al., 2022). Remarkably, even unmanned aerial vehicles (UAVs) can be used for remote image‐based pest identification, which was demonstrated for soybean pests (Tetila et al., 2020).…”

Adams Algorithm with Generative Adversial Network for Maize Leaf Disease Classification

“…In 2023, Yang et al [26] have applied CNN method to detect pest in real-time. Initially, Input images were collected from various maize field with healthy and unhealthy samples.…”