Fruit Orchard Canopy Recognition and Extraction of Characteristics Based on Millimeter-Wave Radar

Yinlong Jiang, Jieli Duan, Yang Li, Jiaxiang Yu, Zhou Yang () and Xing Xu ()
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Yinlong Jiang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Jieli Duan: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Yang Li: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Jiaxiang Yu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Zhou Yang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Xing Xu: College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 13, 1-15

Abstract: Fruit orchard canopy recognition and characteristic extraction are the key problems faced in orchard precision production. To this end, we built a fruit tree canopy detection platform based on millimeter-wave radar, verified the feasibility of millimeter-wave radar from the two perspectives of fruit orchard canopy recognition and canopy characteristic extraction, and explored the detection accuracy of millimeter-wave radar under spray conditions. For fruit orchard canopy recognition, based on the DBSCAN algorithm, an ellipsoid model adaptive clustering algorithm based on a variable-axis (E-DBSCAN) was proposed. The feasibility of the proposed algorithm was verified in the real operation scene of the orchard. The results show that the F 1 score of the proposed algorithm was 96.7%, the precision rate was 93.5%, and the recall rate was 95.1%, which effectively improves the recognition accuracy of the classical DBSCAN algorithm in multi-density point cloud clustering. Regarding the extraction of the canopy characteristics of fruit trees, the RANSAC algorithm and coordinate method were used to extract crown width and plant height, respectively, and a point cloud density adaptive Alpha_shape algorithm was proposed to extract volume. The number of point clouds, crown width, plant height, and volume value under spray conditions and normal conditions were compared and analyzed. The average relative errors of crown width, plant height, and volume were 2.1%, 2.3%, and 4.2%, respectively, indicating that the spray had little effect on the extraction of canopy characteristics by millimeter-wave radar, which could inform spray-related decisions for precise applications.

Keywords: smart agriculture; millimeter-wave radar; canopy recognition; characteristic extraction (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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