Guo Wei, Fukano Yuya, Noshita Koji, Ninomiya Seishi
Institute for Sustainable Agro-Ecosystem Services Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan.
Department of Biology Kyushu University Fukuoka Japan.
Ecol Evol. 2020 Oct 19;10(21):12318-12326. doi: 10.1002/ece3.6861. eCollection 2020 Nov.
Recent advances in Unmanned Aerial Vehicle (UAVs) and image processing have made high-throughput field phenotyping possible at plot/canopy level in the mass grown experiment. Such techniques are now expected to be used for individual level phenotyping in the single grown experiment.We found two main challenges of phenotyping individual plants in the single grown experiment: plant segmentation from weedy backgrounds and the estimation of complex traits that are difficult to measure manually.In this study, we proposed a methodological framework for field-based individual plant phenotyping by UAV. Two contributions, which are weed elimination for individual plant segmentation, and complex traits (volume and outline) extraction, have been developed. The framework demonstrated its utility in the phenotyping of (Jerusalem artichoke), an herbaceous perennial plant species.The proposed framework can be applied to either small and large scale phenotyping experiments.
无人机（UAVs）和图像处理技术的最新进展使得在大规模种植实验中，在小区/冠层水平上进行高通量田间表型分析成为可能。现在预计此类技术将用于单株种植实验中的个体水平表型分析。我们发现在单株种植实验中对单株植物进行表型分析存在两个主要挑战：从杂草背景中分割出植物以及估计难以手动测量的复杂性状。在本研究中，我们提出了一个基于无人机的田间单株植物表型分析方法框架。已开发出两项成果，即用于单株植物分割的杂草消除以及复杂性状（体积和轮廓）提取。该框架在多年生草本植物菊芋的表型分析中证明了其效用。所提出的框架可应用于小规模和大规模的表型分析实验。
