A Study on Leveraging Unmanned Aerial Vehicle Collaborative Driving and Aerial Photography Systems to Improve the Accuracy of Crop Phenotyping

Abstract

Unmanned aerial vehicle (UAV)-based aerial images have enabled a prediction of various factors that affect crop growth. However, the single UAV system leaves much to be desired; the time lag between images affects the accuracy of crop information, lowers the image registration quality and a maximum flight time of 20–25 min, and limits the mission coverage. A multiple UAV system developed from our previous study was used to resolve the problems centered on image registration, battery duration and to improve the accuracy of crop phenotyping. The system can generate flight routes, perform synchronous flying, and ensure capturing and safety protocol. Artificial paddy plants were used to evaluate the multiple UAV system based on leaf area index (LAI) and crop height measurements. The multiple UAV system exhibited lower error rates on average than the single UAV system, with 13.535% (without wind effects) and 17.729–19.693% (with wind effects) for LAI measurements and 5.714% (without wind effect) and 4.418% (with wind effects) for crop’s height measurements. Moreover, the multiple UAV system reduced the flight time by 66%, demonstrating its ability to overcome battery-related barriers. The developed multiple UAV collaborative system has enormous potential to improve crop growth monitoring by addressing long flight time and low-quality phenotyping issues.

Plant phenotyping relevance

複数UAVによる画像取得・飛行協調システムを開発し、LAIと作物高の測定精度を単一UAVと比較評価しており、植物表現型取得手法が中心である。

Code and data availability

The supplied blocks describe UAV flights, crop model dioramas, LAI/height measurement algorithms, and point cloud analyses, but contain no public dataset, image, or code deposit. No data or code availability statement, repository name, or authors' public URL appears anywhere in the text; the only URL is a generic Wingt

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