In Situ Height and Width Estimation of Sorghum Plants from 2.5d Infrared Images

Abstract

Plant phenotyping, or the measurement of plant traits such as stem width and plant height, is a critical step in the development and evaluation of higher yield biofuel crops. Phenotyping allows biologists to quantitatively estimate the biomass of plant varieties and therefore their potential for biofuel production. Manual phenotyping is costly, time-consuming, and errorprone, requiring a person to walk through the fields measuring individual plants with a tape measure and notebook. In this work we describe an alternative system consisting of an autonomous robot equipped with two infrared cameras that travels through fields, collecting 2.5D image data of sorghum plants. We develop novel image processing based algorithms to estimate plant height and stem width from the image data. Our proposed method has the advantage of working in situ using images of plants from only one side. This allows phenotypic data to be collected nondestructively throughout the growing cycle, providing biologists with valuable information on crop growth patterns. Our approach first estimates plant heights and stem widths from individual frames. It then uses tracking algorithms to refine these estimates across frames and avoid double counting the same plant in multiple frames. The result is a histogram of stem widths and plant heights for each plot of a particular genetically engineered sorghum variety. In-field testing and comparison with human collected ground truth data demonstrates that our system achieves 13% average absolute error for stem width estimation and 15% average absolute error for plant height estimation.

Plant phenotyping relevance

赤外線画像と自律ロボットを用いてソルガムの草丈・茎幅を推定する画像処理手法を開発し、地上真値との比較検証も行っており、表現型取得法が研究の中心である。

Code and data availability

The paper describes sorghum phenotyping from 2.5D infrared images collected by a robot, but contains no data availability, code release, or repository statements for its images, phenotype measurements, or analysis code. The only URL present (http://repositories.cdlib.org/lbnl/LBNL-56864) is a cited prior work (Wu et al

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