YOLO-EHS: A lightweight deep learning framework for Xinmei detection and Multi-scale integration in orchard

Aiming at the problem that Xinmei fruits with different maturity levels in the orchard environment appear to overlap and are heavily shaded by tree trunks, branches and leaves, which leads to the difficulty of recognizing fruit maturity, and the complex orchard environment further exacerbates the difficulty of detection, resulting in the detection of the existence of the inability to correctly identify or the problem of low identification accuracy, this study proposes a lightweight Xinmei maturity detection algorithm model based on the improvement of the YOLOv8s model YOLO-EHS. Firstly, the self-developed PSEFR (Pyramid Scale Enhanced Feature Representation) module is proposed to replace the C2F part in BACKBONE, which has the ability of multiscale feature extraction and enhanced feature fusion, reduces the useless computation, and improves the feature extraction ability of Xinmei fruit. Next, the MSETFPN-Neck (Multi-Scale Enhanced and Efficient Feature Pyramid Network) replaces the original Neck with a lightweight design that integrates multi-scale feature-weighted fusion, segmented efficient convolution, a global heterogeneous kernel selection mechanism, and an efficient upsampling module, enhancing multi-scale feature extraction while reducing model parameters and computation. Finally, a lightweight detection head, LSGED (Lightweight Shared Group Enhanced Detection head), is proposed. It integrates shared convolution, group convolution, and detail-enhanced convolution, effectively improving feature representation and detection accuracy. Experimental results show that the optimized model achieves 93.3% average detection accuracy, with improvements of 1.0% in mAP, 1.8% in precision, and 2.2% in recall over the original model. It also reduces computation by 40.5%, parameters by 45.1%, and model size by 40%. This method effectively addresses the challenge of detecting Xinmei fruits in complex orchard environments, significantly enhancing performance and providing strong support for intelligent picking technology.