A new method for measuring the diameter of natural rubber trees using instance segmentation and a monocular RGB camera with non-fixed distances

Abstract

The diameter of natural rubber trees serves as a critical crop parameter, not only for determining whether rubber trees meet tapping requirements and assessing their growth status, but also playing a significant role in calculating parameters such as tapping angle, trajectory, depth and yield prediction. To further advance the intelligent production level of natural rubber trees and achieve low-cost, automated diameter measurement methods, this study proposes a new approach that combines YOLO11s instance segmentation algorithms with a monocular RGB camera to enable non-contact and non-fixed distance diameter measurement of natural rubber trees. The YOLO11-seg is used to obtain masks and bounding boxes for the ID, trunk, and tapped area. This method employs image processing techniques such as contour smoothing, trunk skeleton extraction, angle calculation, and localization. With using the tree ID tag as the primary dimensional reference and incorporating the segmentation contours of trunk categories, it achieves the measurement and calculation of rubber tree trunk diameter. The results demonstrated that among the compared instance segmentation models, the highest segmentation accuracy mAP50-95ˢᵉᵍ reached 0.934. The diameter estimation based on this segmentation and geometric correction process achieved a root mean square error (RMSE) of 2.85 cm and a mean absolute percentage error (MAPE) of 12.58 % under the original measurement conditions. After error compensation, the RMSE and MAPE decreased to 2.13 cm and 8.68 %, respectively. The proposed method can accurately measure the diameter of natural rubber trees, significantly reducing the hardware cost. It provides a new approach for measuring the diameter of natural rubber trees, and also provides both theoretical support and practical basis for the intelligent production and precision agriculture in natural rubber cultivation.

Plant phenotyping relevance

ゴム樹の幹径という植物形態形質を、RGB画像とインスタンスセグメンテーションで非接触・自動推定する手法を開発し、精度検証まで行っており、フェノタイピング手法が中心である。

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