School of Biology and Environmental Science, University College Dublin, Dublin, Ireland.
School of Computer Science and UCD Energy Institute, University College Dublin, Dublin, Ireland.
Open Biol. 2022 Jun;12(6):210353. doi: 10.1098/rsob.210353. Epub 2022 Jun 22.
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.
农民和育种家旨在提高作物对非生物胁迫的响应能力，并在不利的环境条件下确保产量。为了实现这一目标并选择最具弹性的基因型，植物育种家和研究人员依赖表型分析来量化作物对非生物胁迫的响应。成像技术的最新进展使研究人员能够在整个时间内非破坏性地收集生理数据，从而有可能将复杂的植物响应分解为可量化的特征。基于图像的技术的使用使得可以在受控环境条件和田间试验中量化作物对胁迫的响应。本文总结了已用于评估不同非生物胁迫（包括盐度、干旱和氮缺乏）的表型成像技术（RGB、多光谱和高光谱传感器等），同时讨论了它们的优缺点。我们详细回顾了在高通量表型设施下或使用无人机在田间通过一系列成像传感器进行量化的耐非生物胁迫的特征。我们还提供了光谱耐量指数的最新汇编，并讨论了机器学习的进展和挑战，包括监督和无监督模型以及深度学习。
