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Design and experiment of a binocular vision-based canopy volume extraction system for precision pesticide application by UAVs

文献类型： 外文期刊

作者： Zhang, Ruirui 1 ; Lian, Shuaikai 2 ; Li, Longlong 1 ; Zhang, Linhuan 1 ; Zhang, Chaocheng 1 ; Chen, Liping 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

2.Natl Ctr Int Res Agr Aerial Applicat Technol, Beijing 100097, Peoples R China

3.Shanghai Univ, Shanghai 200444, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

关键词： Binocular vision; Fruit tree canopy; Profile modeling; Volume calculation

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2023 年 213 卷

页码：

收录情况： SCI

摘要： When unmanned aerial vehicles (UAVs) are used for orchard chemicals application, accurate measurement of the canopy volume can provide decision support for determining pesticide dosages, flight parameters, and droplet sizes. Using binocular camera ranging, this study presents a novel canopy segmentation algorithm that preprocesses light detection ranging data to extract sub-grid canopy volumes. A binocular vision-based canopy volume extraction system for UAV chemical application was developed. The system utilizes multi-degree-offreedom adaptive balance technology to ensure that the binocular camera can still vertically detect the canopy even when the flight attitude changes. Performance experiments were conducted using artificial fruit trees with different leaf densities and regular cardboard box as measurement targets. The canopy volume measurements indicate that the new model accurately detects target contours. When flying at 2 m/s, the maximum errors between system-measured and actual volumes were 6.58 and 9.37 % for the rectangular and triangular, respectively. Increasing speeds and attitudes lead to increased errors and measurement variations. However, the position of the system relative to the target does not cause significant differences in results. The maximum measurement errors between system-measured and actual LiDAR values were 6.44 and 9.17 % for high- and lowdensity canopies, respectively. These results demonstrate that the proposed system has high measurement accuracy and provides a reliable precision UAV pesticide-spraying control system for plant protection based on real-time canopy detection.

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