YOLO-RSTS: a precise segmentation model for detecting preservative and stimulant spraying regions on rubber trees.

ABSTRACT: The application of preservatives and ethylene stimulants is critical for improving latex yield and extending the lifespan of rubber trees; however, traditional manual spraying methods are inefficient and unsuitable for large-scale plantation management. Moreover, existing segmentation models are challenged by complex bark textures and varying illumination conditions, resulting in blurred spraying boundaries and reduced recognition accuracy. To address these issues, this study proposes an improved segmentation model based on the YOLOv12n-Seg framework, termed YOLO-RSTS (YOLO for Rubber Spraying Target Segmentation), for accurately distinguishing preservative and stimulant spraying regions on rubber trees. The proposed model introduces three novel modules: CrossScaleDSC, CG-Attention, and C2

SUBMITTER: Zhu J 

PROVIDER: S-EPMC12819190 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature