In situ width estimation of biofuel plant stems

Abstract

Efficient plant phenotyping methods are necessary in order to accelerate the development of high yield biofuel crops. Manual measurement of plant phenotypes, such as width, is slow and error-prone. We propose a novel approach to estimating the width of corn and sorghum stems from color and depth images obtained by mounting a camera on a robot which traverses through plots of plants. We use deep learning to detect individual stems and employ an image processing pipeline to model the boundary of each stem and estimate the pixel and metric width of each stem. This approach results in 13.5% absolute error in the pixel domain on corn averaged over 153 estimates and 13.2% metric absolute error on phantom sorghum averaged over 149 estimates.

Plant phenotyping relevance

植物茎径という形態形質を、ロボット搭載カメラのカラー・深度画像と画像処理で推定する手法を開発・評価しており、フェノタイピング手法が中心である。

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