Multiscale Heterogeneous Fusion for Lightweight Detection of Cotton Growth Stages in Complex Field Imagery

Background: Accurate recognition of cotton growth stages in complex field environments is crucial for optimizing crop management and harvest scheduling. Methods: In this study, a lightweight multiscale heterogeneous fusion detection network, RT-HEM-Li Fusion, was constructed based on the RT-DETR framework to identify five cotton growth stages, namely cotton bud, cotton blossom, early boll, matured cotton boll, and split cotton boll. The network was evaluated on a publicly available cotton growth dataset. To enhance feature representation capability and small-object detection performance, an Inverted Residual Mobile Block was introduced into the ResNet-18 backbone, an adaptive dual-path aggregation pyramid with trident kernels was designed to preserve cross-scale details, and the baseline GIoU loss was replaced with WIoU-Focaler-NWD to enhance small-object localization and class discrimination. Results: RT-HEM-Li Fusion achieved the highest overall [email protected] of 0.782 among the compared models, ahead of TridentNet (0.751) and RT-DETR (0.722) on average, while requiring only 13.905 M parameters and 40.2 GFLOPs, approximately one twentieth of the computational cost of TridentNet and fewer parameters than YOLOv5 (25.048 M), YOLOv8 (25.843 M), and DINO (47.548 M). Conclusions: RT-HEM-Li Fusion provides an effective image-based tool for monitoring cotton growth stages, demonstrating stable detection performance under the complex field conditions represented in the evaluated dataset, with a favorable balance between accuracy and model complexity.