Soltabayeva Aigerim, Ongaltay Assel, Omondi John Okoth, Srivastava Sudhakar
Biology Department, School of Science and Humanities, Nazarbayev University, Nur Sultan Z05H0P9, Kazakhstan.
International Institute of Tropical Agriculture, PO Box 30258 Lilongwe 3, Malawi.
Plants (Basel). 2021 Jan 27;10(2):243. doi: 10.3390/plants10020243.
Plant growth and development is adversely affected by different kind of stresses. One of the major abiotic stresses, salinity, causes complex changes in plants by influencing the interactions of genes. The modulated genetic regulation perturbs metabolic balance, which may alter plant's physiology and eventually causing yield losses. To improve agricultural output, researchers have concentrated on identification, characterization and selection of salt tolerant varieties and genotypes, although, most of these varieties are less adopted for commercial production. Nowadays, phenotyping plants through Machine learning (deep learning) approaches that analyze the images of plant leaves to predict biotic and abiotic damage on plant leaves have increased. Here, we review salinity stress related markers on molecular, physiological and morphological levels for crops such as maize, rice, ryegrass, tomato, salicornia, wheat and model plant, . The combined analysis of data from stress markers on different levels together with image data are important for understanding the impact of salt stress on plants.
植物的生长和发育受到各种不同胁迫的不利影响。盐害作为主要的非生物胁迫之一,通过影响基因间的相互作用,在植物体内引发复杂的变化。这种被调节的基因调控扰乱了代谢平衡,进而可能改变植物的生理机能并最终导致产量损失。为了提高农业产量,研究人员专注于耐盐品种和基因型的鉴定、特征描述及选择,尽管这些品种大多较少应用于商业生产。如今,通过机器学习(深度学习)方法对植物进行表型分析的情况有所增加,这些方法通过分析植物叶片图像来预测植物叶片上的生物和非生物损伤。在此,我们综述了玉米、水稻、黑麦草、番茄、盐角草、小麦等作物以及模式植物在分子、生理和形态水平上与盐胁迫相关的标记。来自不同水平胁迫标记的数据与图像数据的综合分析,对于理解盐胁迫对植物的影响至关重要。