Citrus instance segmentation and yield prediction based on parallel multiscale channel enhanced

Citrus fruit instance segmentation is an important prerequisite for citrus fruit detection, recognition, and yield estimation in complex environments of citrus orchards. In order to address the challenges of leaf shading, fruit overlapping, and light variations in citrus fruit detection, identification, and yield estimation, this study proposes the YOLO11n-LMPS based on the improved You Only Look Once-11n-Segmentation (YOLO11n-Seg) for citrus instance segmentation and yield estimation. The YOLO11n-LMPS proposes a Parallel Multi-scale Channel Enhancement (PMCE) module for enhancing different sizes of fruit feature extraction and suppressing background noise. An improved Lightweight Separated and Enhancement Attention Module (LSEAM) is used in the Cross Stage Partial with Pyramid Squeeze Attention (C2PSA) to enhance attention and masking robustness and reduce computational complexity. The Multi-Scale Dilated Attention (MSDA) is introduced to optimise detail and global semantic information capture and extend the sensory field. The experimental results in Section 3.2 show that the mAPmask 50 of the YOLO11n-LMPS reaches 91.6%, and the mAPmask 50∶95 reaches 71.1%. The mAPbox 50 of the YOLO11n-LMPS reaches 92.1%, and the mAPbox 50∶95 reaches 74.1%. mAPmask 50, mAPmask 50∶95, mAPbox 50and mAPbox 50∶95 are higher than other mainstream models. Under the complex orchard environment, YOLO11n-LMPS improves citrus fruit detection and segmentation accuracy in complex orchard scenarios, offering a reliable solution for yield estimation.