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Advanced Plant Phenotyping: Unmanned Aerial Vehicle Remote Sensing and CimageA Software Technology for Precision Crop Growth Monitoring

文献类型： 外文期刊

作者： Fu, Hongyu 1 ; Lu, Jianning 1 ; Cui, Guoxian 1 ; Nie, Jihao 1 ; Wang, Wei 1 ; She, Wei 1 ; Li, Jinwei 1 ;

作者机构： 1.Hunan Agr Univ, Coll Agr, Dept Intelligent Agr, Changsha 410128, Peoples R China

2.Hunan Acad Agr Sci, Inst Agr Environm & Ecol, Changsha 410125, Peoples R China

关键词： UAV remote sensing; data processing software; non-destructive monitoring; plant phenotyping

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 11 期

页码：

收录情况： SCI

摘要： In production activities and breeding programs, large-scale investigations of crop high-throughput phenotype information are needed to help improve management and decision-making. The development of UAV (unmanned aerial vehicle) remote sensing technology provides a new means for the large-scale, efficient, and accurate acquisition of crop phenotypes, but its practical application and popularization are hindered due to the complicated data processing required. To date, there is no automated system that can utilize the canopy images acquired through UAV to conduct a phenotypic character analysis. To address this bottleneck, we developed a new scalable software called CimageA. CimageA uses crop canopy images obtained by UAV as materials. It can combine machine vision technology and machine learning technology to conduct the high-throughput processing and phenotyping of crop remote sensing data. First, zoning tools are applied to draw an area-of-interest (AOI). Then, CimageA can rapidly extract vital remote sensing information such as the color, texture, and spectrum of the crop canopy in the plots. In addition, we developed data analysis modules that estimate and quantify related phenotypes (such as leaf area index, canopy coverage, and plant height) by analyzing the association between measured crop phenotypes and CimageA-derived remote sensing eigenvalues. Through a series of experiments, we confirmed that CimageA performs well in extracting high-throughput remote sensing information regarding crops, and verified the reliability of retrieving LAI (R2 = 0.796) and estimating plant height (R2 = 0.989) and planting area using CimageA. In short, CimageA is an efficient and non-destructive tool for crop phenotype analysis, which is of great value for monitoring crop growth and guiding breeding decisions.

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