Work

Branch length recognition and pruning point localization method for greenhouse tomatoes based on improved YOLOv8

Year: 2025

Type: article

Abstract: • Length recognition and pruning point localization using improved YOLOv8. • Instance segmentation within detection box to minimize background influence. • Total sucker branch length calculated via pixel height ratio. • Average absolute error of 0.411 cm, RMSE of 0.494 cm in length measurement. • Pruning point localization success rate of 92 %, with 0.247 cm error. To achieve precise identification, length estimation, and pruning point localization for robotic tomato pruning, this study presents a method for branch length recognition and pruning point localization based on an improved YOLOv8 model. The proposed YOLOv8n-CE integrates the CBAM attention mechanism to enhance the model’s focus on critical branch features, and replaces the original loss with the EIOU loss to improve bounding box regression accuracy and convergence speed. Instance segmentation is subsequently performed within the detection boxes to obtain the main stem mask and extract the upper and lower endpoints of the stem. The pixel coordinates of these endpoints are transformed into three-dimensional camera coordinates to compute the stem length, and the actual branch length is derived by combining the pixel height ratio of the bounding box with the segmentation mask. The pruning point is localized along the central line of the main stem, 1.5 cm above the lower endpoint, ensuring reduced pathogen intrusion and faster wound healing. Experimental results demonstrate that the YOLOv8n-CE model improves mAP₅₀ by 1.7 percentage points compared with YOLOv8n. For branch length measurement, the method achieves an R² of 0.929, an MAE of 0.411 cm, and an RMSE of 0.494 cm. The pruning point localization success rate reaches 92%, with a mean absolute error of 0.247 cm. These results verify that the proposed approach meets the accuracy requirements for tomato branch measurement and pruning point localization, providing a reliable theoretical and technical foundation for robotic pruning applications.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2025.101739