Capturing crop adaptation to abiotic stress using image-based technologies

Abstract

Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.

Plant phenotyping relevance

作物の非生物的ストレス応答を定量化する画像ベースの表現型解析技術を中心に、センサー、形質、指標、機械学習を体系的にレビューしているため。

Code and data availability

This is a review article on image-based phenotyping of abiotic stress. The authors explicitly state 'This article has no additional data.' All URLs in the text refer to phenotyping infrastructure networks (APPF, EPPN, NAPPN, IPPN, EMPHASIS), a spectral index database, and external open datasets (PlantVillage) cited as旁

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