EMSC-YOLO: A Lightweight Model for Detecting Litchi Maturity in Complex Orchard Environments

This study focuses on developing a lightweight litchi maturity detection model for complex orchard environments. Maturity detection in orchards remains challenging due to variable illumination, dense fruit clustering, and subtle visual differences among maturity stages. Moreover, many existing approaches rely on heavyweight architectures with high computational costs, limiting deployment on resource-constrained edge devices in agriculture. To address these issues, we propose EMSC-YOLO, a lightweight and efficient detector for litchi maturity detection. The model incorporates a novel Efficient Multi-Scale Convolution (EMSConv) module to enhance multi-scale feature representation while reducing redundant computation and parameters. In addition, the standard C2f module is redesigned as C2f-EMSC to improve feature reuse and cross-channel interaction within a compact architecture, enabling effective detection of densely distributed litchi fruits. Experiments on the Complex Orchard Environments Oriented Litchi Maturity (COEOLM) dataset demonstrate the effectiveness of the proposed approach. Compared with YOLOv10n on the COEOLM test set, EMSC-YOLO achieves a precision of 87.7% and reduces the number of parameters by 15.5% to only 4.02 M. EMSC-YOLO improves [email protected] by 2.81 percentage points and 1.48 percentage points over YOLOv10n and YOLO11, respectively. Meanwhile, EMSC-YOLO maintains a high inference speed of 129.32 FPS, with only a 3.69% relative decrease in FPS compared with the baseline YOLOv10n whose FPS is 134.28, satisfying the real-time detection requirements of orchard edge scenarios. Overall, the proposed method provides a deployable solution for real-time litchi maturity detection in complex orchard environments and can be extended to maturity detection tasks for other clustered fruit crops.