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Frequent pest infestations in rice can substantially decrease rice yield, severely hindering agricultural production. Computer vision can assist farmers in timely locating and removing partial or even entire plants infested with pests, achieving pest control. However, the size and population density variance of paddy pests commonly cause interclass and intraclass differences in pest datasets, as well as an imbalance in hard samples. In addition, existing deep learning models struggle to balance compactness with high detection accuracy. This paper addresses these challenges by constructing the GPest14 dataset using FastGAN image generation technology, web crawlers, and manual selection. This is a large-scale rice pest dataset containing 14 categories suitable for training fine-grained visual detection tasks of rice pests. This work proposes the Fully Connected Bottleneck Transformer (FCBT) module, implants it into the tail of the YOLOv8n architecture backbone, and constructs the FCBTYOLO object detection model. Results indicate that the Gpest14 dataset increases by at least 12.2% in mean Average Precision (mAP) across major detection models. Simultaneously, our proposed FCBTYOLO model can achieve an average precision (mAP@50) of 93.6% with the network training weights constrained to a mere 6.7MB, and it only requires 16.8 milliseconds to detect a single pest image. FCBTYOLO has been validated on the large-scale public Pest24 dataset, where it can achieve a 1.1% improvement in mAP over the YOLOv8n model while maintaining the training weight at 8.3MB. Compared with existing pest detection models, FCBTYOLO exhibits lighter, superior performance and is more suitable for field-embedded deployment in digital agriculture.

FCBTYOLO: A Lightweight and High-Performance Fine Grain Detection Strategy for Rice Pests