Canopy Segmentation of Overlapping Fruit Trees Based on Unmanned Aerial Vehicle LiDAR

Shiji Wang, Jie Ji (), Lijun Zhao, Jiacheng Li, Mian Zhang and Shengling Li
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Shiji Wang: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Jie Ji: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Lijun Zhao: School of Intelligent and Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China
Jiacheng Li: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Mian Zhang: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Shengling Li: College of Engineering and Technology, Southwest University, Chongqing 400715, China

Agriculture, 2025, vol. 15, issue 3, 1-21

Abstract: Utilizing LiDAR sensors mounted on unmanned aerial vehicles (UAVs) to acquire three-dimensional data of fruit orchards and extract precise information about individual trees can greatly facilitate unmanned management. To address the issue of low accuracy in traditional watershed segmentation methods based on canopy height models, this paper proposes an enhanced method to extract individual tree crowns in fruit orchards, enabling the improved detection of overlapping crown features. Firstly, a distribution curve of single-row or single-column treetops is fitted based on the detected treetops using variable window size. Subsequently, a cubic spatial region extending infinitely along the Z-axis is generated with equal width around this curve, and all crown points falling within this region are extracted and then projected onto the central plane. The projecting contour of the crowns on the plane is then fitted using Gaussian functions. Treetops are detected by identifying peak points on the curve fitted by Gaussian functions. Finally, the watershed algorithm is applied to segment fruit tree crowns. The results demonstrate that in citrus orchards with pronounced crown overlap, this novel method significantly reduces the number of undetected trees with a recall of 97.04%, and the F1 score representing the detection accuracy for fruit trees reaches 98.01%. Comparisons between the traditional method and the Gaussian fitting–watershed fusion algorithm across orchards exhibiting varying degrees of crown overlap reveal that the fusion algorithm achieves high segmentation accuracy when dealing with overlapping crowns characterized by significant height variations.

Keywords: overlapping fruit crown segmentation; Gaussian fitting; treetop detection; watershed algorithm (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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