A key challenge in automated marigold harvesting lies in the accurate identification of picking points under complex environmental conditions, such as dense shading and intense illumination. To tackle this problem, this research proposes a lightweight instance segmentation model combined with a harvest position estimation method. Based on the YOLOv11n-seg segmentation framework, we develop a lightweight PDS-YOLO model through two key improvements: (1) structural pruning of the base model to reduce its parameter count, (2) incorporation of a Channel-wise Distillation (CWD)-based feature distillation method to compensate for the accuracy loss caused by pruning. The resulting lightweight segmentation model achieves a size of only 1.3 MB (22.8% of the base model) and a computational cost of 5 GFLOPs (49.02% of the base model). At the same time, it maintains high segmentation performance, with a precision of 93.6% and a mean average precision (mAP) of 96.7% for marigold segmentation. Furthermore, the proposed model demonstrates enhanced robustness under challenging scenarios including strong lighting, cloudy weather, and occlusion, improving the recall rate by 1.1% over the base model. Based on the segmentation results, a method for estimating marigold harvest positions using 3D point clouds is proposed. Fitting and deflection angle experiments confirm that the fitting errors are constrained within 3–12 mm, which lies within an acceptable range for automated harvesting. These results validate the capability of the proposed approach to accurately locate marigold harvest positions under top-down viewing conditions. The lightweight segmentation network and harvest position estimation method presented in this work offer effective technical support for selective harvesting of marigolds.
Ma et al. (Sat,) studied this question.