Research on a Multi-Sensor Fusion-Based Method for Fruit-Tree Dripline Path Detection

Wei, Daochu; Wang, Zhichong; Wang, Jingwei; Li, Xuecheng; Zou, Wei; Zhai, Changyuan

doi:10.3390/agronomy16010020

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Research on a Multi-Sensor Fusion-Based Method for Fruit-Tree Dripline Path Detection

by

Daochu Wei

^1,2,

Zhichong Wang

²,

Jingwei Wang

²,

Xuecheng Li

²,

Wei Zou

^2,* and

Changyuan Zhai

^1,2,*

¹

College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China

²

Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

^*

Authors to whom correspondence should be addressed.

Agronomy 2026, 16(1), 20; https://doi.org/10.3390/agronomy16010020 (registering DOI)

Submission received: 17 November 2025 / Revised: 15 December 2025 / Accepted: 19 December 2025 / Published: 21 December 2025

(This article belongs to the Special Issue Advances in Precision Pesticide Spraying Technology and Equipment)

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Abstract

To enable automatic extraction of high-precision paths for intelligent orchard operations, a path detection method targeting the fruit-tree dripline is proposed. The method integrates 2D-LiDAR, RTK-GNSS, and an electronic compass, achieving time synchronization, coordinate-frame construction, and extrinsic calibration. Point clouds are rotation-normalized via least-squares trajectory fitting; ground segmentation and statistical filtering suppress noise; segment-wise extremal edge points, together with an α-shape-based concave hull algorithm, fit and generate the dripline path; and inverse rotation restores the result to the orchard-local coordinate frame. Field experiments demonstrated that the method accurately extracts dripline paths in orchard environments; relative to manual measurements, the overall mean absolute error was 0.23 m and the root-mean-square error was 0.30 m. Across different travel speeds, the system exhibited good adaptability and stability, meeting the path-planning requirements of precision orchard operations.

Keywords: fruit-tree dripline; 2D-LiDAR; multi-sensor fusion; 3D reconstruction; dripline path

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MDPI and ACS Style

Wei, D.; Wang, Z.; Wang, J.; Li, X.; Zou, W.; Zhai, C. Research on a Multi-Sensor Fusion-Based Method for Fruit-Tree Dripline Path Detection. Agronomy 2026, 16, 20. https://doi.org/10.3390/agronomy16010020

AMA Style

Wei D, Wang Z, Wang J, Li X, Zou W, Zhai C. Research on a Multi-Sensor Fusion-Based Method for Fruit-Tree Dripline Path Detection. Agronomy. 2026; 16(1):20. https://doi.org/10.3390/agronomy16010020

Chicago/Turabian Style

Wei, Daochu, Zhichong Wang, Jingwei Wang, Xuecheng Li, Wei Zou, and Changyuan Zhai. 2026. "Research on a Multi-Sensor Fusion-Based Method for Fruit-Tree Dripline Path Detection" Agronomy 16, no. 1: 20. https://doi.org/10.3390/agronomy16010020

APA Style

Wei, D., Wang, Z., Wang, J., Li, X., Zou, W., & Zhai, C. (2026). Research on a Multi-Sensor Fusion-Based Method for Fruit-Tree Dripline Path Detection. Agronomy, 16(1), 20. https://doi.org/10.3390/agronomy16010020

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Research on a Multi-Sensor Fusion-Based Method for Fruit-Tree Dripline Path Detection

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