Screening and identification of heat tolerance in Indian wheat genotypes using generalized regression neural network (GRNN) model

Anil Kumar Sadaf Fatima Iffat Azim Anshika Negi Shalini Bhadola Pooja Jha Suboot Hairat   

Open Access   

Published:  May 03, 2024

DOI: 10.7324/JABB.2024.167028

The present study using a generalized regression neural network (GRNN) model was carried out to check the effect of high temperature on five Indian bread wheat genotypes, namely, C306, K7903, CBW12, HD2329, and HD2428, and to develop an algorithm for the screening and identification of heat tolerance in wheat. A highly significant differential survivability in response to the temperature induction response technique with germinating seeds was observed for the five wheat genotypes. Maximum survivability was observed for C306 and K7903, whereas HD2329 and HD2428 showed minimum survival in response to lethal temperature stress. Similarly, at the mature plant stage, in response to heat treatment of 34°C/26°C (day/night), all five wheat genotypes showed a significant difference in chlorophyll a fluorescence, total chlorophyll content, and the reduction of 1,1- diphenyl-2-picryl-hydrazyl (DPPH) in response to heat treatment. The genotypes, C306, K7903, and CBW12 showed better performance for all the chlorophyll a fluorescence parameters under study including Fo, Fv/Fm, qL, qP, NPQ, θPSII, ETR, along with the total chlorophyll content and DPPH reduction, in contrast, the genotypes, HD2329 and HD2428 performed poorly for all parameters including chlorophyll a fluorescence parameters, total chlorophyll, and DPPH reduction. Higher inflection temperature and peak temperature were observed in C306 and K7903, in contrast, HD2329 and HD2428 showed significantly lower inflection temperature and peak temperature. Higher expression of the selected genes involved in photosynthesis and ROS scavenging was observed in C306 and K7903 relative to HD2329 and HD2428. On the basis of the different chlorophyll a fluorescence parameters, the algorithm was developed for the calculation of the heat susceptibility index. The algorithm was independently checked for the identification and segregation of all the five wheat genotypes taken in this study as tolerant, moderate, and susceptible. For all the genotypes, the algorithm was able to predict the heat susceptibility of the genotypes with high accuracy. Hence, the algorithm in combination with chlorophyll a fluorescence can be used for the screening of wheat and other plant species in response to abiotic stress, especially heat stress.

Keyword:     Chlorophyll a fluorescence heat susceptibility index GRNN model Temperature Induction Response wheat


Kumar A, Fatima S, Azim I, Negi A, Bhadola S, Jha P, Hairat S. Screening and identification of heat tolerance in Indian wheat genotypes using generalized regression neural network (GRNN) model. J App Biol Biotech. 2024. Online First.

Copyright: Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.

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