Capturing crop adaptation to abiotic stress using image-based technologies. Issue 6 (22nd June 2022)

• Record Type: Journal Article
• Authors: Al-Tamimi, Nadia; Langan, Patrick; Bernád, Villő; Walsh, Jason; Mangina, Eleni; Negrão, Sónia
• Is Part Of: Open biology. Volume 12:Issue 6(2022)
• Publication Date: 2022-06-22
• Subjects: abiotic stress -- imaging -- high-throughput phenotyping -- crops -- machine learning; Biology -- Periodicals; 570
• DOI: 10.1098/rsob.210353
• Languages: English
• ISSNs: 2046-2441

Abstract

Abstract : 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.