Length phenotyping with interest point detection

Abstract

Plant phenotyping is the task of measuring plant attributes mainly for agricultural purposes. We term length phenotyping the task of measuring the length of a plant part of interest. The recent rise of low cost RGB-D sensors and accurate deep artificial neural networks provides new opportunities for length phenotyping. We present a general technique for length phenotyping based on three stages: object detection, point of interest identification, and a 3D measurement phase. We address object detection and interest point identification by training network models for each task, and develop a robust de-projection procedure for the 3D measurement stage. We apply our method to three real world tasks: measuring the height of a banana tree, the length and width of banana leaves in potted plants, and the length of cucumbers fruits in field conditions. The three tasks were solved using the same pipeline with minor adaptations, indicating the method’s general potential. The method is stagewise analyzed and shown to be preferable to alternative algorithms, obtaining error of less than 10% deviation in all tasks. For leaves’ length and width, the measurements are shown to be useful for further phenotyping of plant treatment and mutant classification.

Plant phenotyping relevance

植物部位の長さをRGB-Dセンサー、物体検出、関心点検出、3D計測で推定する汎用フェノタイピング手法の開発・評価が中心である。

Code and data availability

公開本文の所在を確認できませんでした。非公開または購読が必要な可能性があります。

No evidence-backed public reproduction asset is currently recorded.