Quantifying the Onset and Progression of Plant Senescence by Color Image Analysis for High Throughput Applications.

Abstract

Leaf senescence, an indicator of plant age and ill health, is an important phenotypic trait for the assessment of a plant's response to stress. Manual inspection of senescence, however, is time consuming, inaccurate and subjective. In this paper we propose an objective evaluation of plant senescence by color image analysis for use in a high throughput plant phenotyping pipeline. As high throughput phenotyping platforms are designed to capture whole-of-plant features, camera lenses and camera settings are inappropriate for the capture of fine detail. Specifically, plant colors in images may not represent true plant colors, leading to errors in senescence estimation. Our algorithm features a color distortion correction and image restoration step prior to a senescence analysis. We apply our algorithm to two time series of images of wheat and chickpea plants to quantify the onset and progression of senescence. We compare our results with senescence scores resulting from manual inspection. We demonstrate that our procedure is able to process images in an automated way for an accurate estimation of plant senescence even from color distorted and blurred images obtained under high throughput conditions.

Plant phenotyping relevance

植物の老化を画像から自動推定する補正・復元・解析手法を開発し、手動評価と比較検証しており、表現型取得が研究の中心です。

Code and data availability

保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。