GBiDC-PEST: A novel lightweight model for real-time multiclass tiny pest detection and mobile platform deployment

Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection. However, their deployment on mobile devices has been constrained by high computational demands. Here, we developed GBiDC-PEST, a mobile application that incorporates an improved, lightweight detection algorithm based on the You Only Look Once (YOLO) series single-stage architecture, for real-time detection of four tiny pests (wheat mites, sugarcane aphids, wheat aphids, and rice planthoppers). GBiDC-PEST incorporates several innovative modules, including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone, the bi-directional feature pyramid network (BiFPN) for enhanced multiscale feature fusion, depthwise convolution (DWConv) layers to reduce computational load, and the convolutional block attention module (CBAM) to enable precise feature focus. The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset (Tpest-3960) that covered various field environments. GBiDC-PEST (2.8 MB) significantly reduced the model size to only 20% of the original model size, offering a smaller size than the YOLO series (v5–v10), higher detection accuracy than YOLOv10n and v10s, and faster detection speed than v8s, v9c, v10m and v10b. In Android deployment experiments, GBiDC-PEST demonstrated enhanced performance in detecting pests against complex backgrounds, and the accuracy for wheat mites and rice planthoppers was improved by 4.5–7.5% compared with the original model. The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid, onsite identification and localization of tiny pests. This advancement provides valuable insights for effective pest monitoring, counting, and control in various agricultural settings.