GrainPest-SSL: A Lightweight Semi-Supervised Detector for Stored-Grain Pest Monitoring in Smart Granaries

Reliable stored-grain pest monitoring is essential for smart granaries, yet probe-based field images pose three coupled bottlenecks: tiny and densely distributed pests in complex backgrounds, costly bounding-box annotation, and limited edge-side computing resources. To address these bottlenecks in a targeted manner, this study proposes GrainPest-SSL, an integrated framework comprising a field dataset, a lightweight detector, and a pseudo-label purification-based semi-supervised pipeline. First, to overcome the lack of realistic training data, a GrainPest dataset with 1000 field images and 21,676 annotated pest instances is constructed using multiple self-developed monitoring probes deployed in a large wheat flat granary, capturing systematic pest-monitoring images from different in-bin locations rather than a single fixed imaging point. Second, to improve small-target detection under resource constraints, a YOLOv8n-CAEMA detector is designed with a P2 detection head and tail-inserted Coordinate Attention (CA) and Efficient Multi-scale Attention (EMA), achieving 0.840 mAP@0.5 under full supervision with only 2.932 M parameters. Third, to reduce annotation dependence without adding inference-stage complexity, an offline Teacher–Student strategy with Pseudo-Label Purification Filtering (PPLF) refines pseudo-labels using confidence, size, and aspect-ratio priors; under the 30% labeled setting, GrainPest-SSL improves mAP@0.5 from 0.738 to 0.799 and mAP@0.5:0.95 from 0.322 to 0.369 on average over three random seeds. Comparisons with representative agricultural pest detectors and semi-supervised object detection (SSOD) methods further confirm the balanced accuracy–efficiency performance of GrainPest-SSL under label-limited conditions. The deployed Student detector further achieves 13.6 FPS in FP16 mode on a Jetson Orin Nano Dev Kit under the 10 W power mode, supporting scheduled pest inspection, early infestation screening, and intelligent warning in smart granary monitoring systems.