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Real-time detection and characterization of trunks and upright branches of pear trees for automatic dormant pruning.

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Bibliographic Details
Title:	Real-time detection and characterization of trunks and upright branches of pear trees for automatic dormant pruning.
Authors:	Sun, Hao¹ (AUTHOR), Wu, Gengchen¹ (AUTHOR), Xu, Hu¹ (AUTHOR), Li, Jiaqi¹ (AUTHOR), Zhou, Zhidi¹ (AUTHOR), Tao, Shutian^2,3 (AUTHOR), Guo, Wei⁴ (AUTHOR), Qi, Kaijie² (AUTHOR), Yin, Hao² (AUTHOR), Zhang, Shaoling² (AUTHOR), Ninomiya, Seishi^1,4 (AUTHOR), Mu, Yue¹ (AUTHOR) yuemu@njau.edu.cn
Source:	Precision Agriculture. Apr2026, Vol. 27 Issue 2, p1-32. 32p.
Subject Terms:	Tree pruning, Tree branches, Orchard management, Cameras, *Object recognition (Computer vision)
Abstract:	Purpose: Dormant pruning is critical for fruit tree management and maintaining fruit quality. Traditional manual pruning is labor-intensive, driving interest in automated robotic dormant pruning. However, automated robotic dormant pruning meets significant challenges in trunk and branches detection, due to the complexity of the orchard environment and the interlacing of the branches. This paper proposes an automatic method for real-time detection and pruning of pear tree trunks and upright branches using an RGB-D camera. Methods: Pear trunk detection was conducted by enhancing the You Only Look Once version 5 Nano (YOLOv5n) model with Squeeze-and-Excitation Networks (SENet) and optimizing the anchor boxes. For branch segmentation, YOLOv8n-seg integrated with Dynamic Snake Convolution (DSConv) and Focal Scale Intersection over Union (Focal_SIoU) was employed. The angle of the branches was calculated from the generated mask images, and the pruning position was determined with depth information. A PRUNING_ROS package was developed for real-time orchard applications. Results: The evaluation demonstrated 96.7% mean average precision (mAP) for trunk detection and 82.6% mAP for branch segmentation in test dataset. Field test results showed that the mean absolute error (MAE) of trunk distance localization compared to manual measurements was 3.71 cm, with the root mean square error (RMSE) of 3.84 cm, and the frames per second (FPS) of 31.6. The MAE was 2.3 cm (RMSE: 2.6 cm) for pruning points and 2.41° (RMSE: 2.78°) for upright branches angle, with the FPS of 36.2. The field pruning experiment showed a pruning success rate of 47.6%. Conclusion: This method provides technical support for the operation of fruit tree pruning robots, representing a step toward the full automation of fruit tree management. [ABSTRACT FROM AUTHOR]
Database:	Energy & Power Source

Description
Abstract:	Purpose: Dormant pruning is critical for fruit tree management and maintaining fruit quality. Traditional manual pruning is labor-intensive, driving interest in automated robotic dormant pruning. However, automated robotic dormant pruning meets significant challenges in trunk and branches detection, due to the complexity of the orchard environment and the interlacing of the branches. This paper proposes an automatic method for real-time detection and pruning of pear tree trunks and upright branches using an RGB-D camera. Methods: Pear trunk detection was conducted by enhancing the You Only Look Once version 5 Nano (YOLOv5n) model with Squeeze-and-Excitation Networks (SENet) and optimizing the anchor boxes. For branch segmentation, YOLOv8n-seg integrated with Dynamic Snake Convolution (DSConv) and Focal Scale Intersection over Union (Focal_SIoU) was employed. The angle of the branches was calculated from the generated mask images, and the pruning position was determined with depth information. A PRUNING_ROS package was developed for real-time orchard applications. Results: The evaluation demonstrated 96.7% mean average precision (mAP) for trunk detection and 82.6% mAP for branch segmentation in test dataset. Field test results showed that the mean absolute error (MAE) of trunk distance localization compared to manual measurements was 3.71 cm, with the root mean square error (RMSE) of 3.84 cm, and the frames per second (FPS) of 31.6. The MAE was 2.3 cm (RMSE: 2.6 cm) for pruning points and 2.41° (RMSE: 2.78°) for upright branches angle, with the FPS of 36.2. The field pruning experiment showed a pruning success rate of 47.6%. Conclusion: This method provides technical support for the operation of fruit tree pruning robots, representing a step toward the full automation of fruit tree management. [ABSTRACT FROM AUTHOR]
ISSN:	13852256
DOI:	10.1007/s11119-025-10310-9