Browsing by Author "Srushtideep Angidi"

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Advanced High-Throughput Phenotyping Techniques for Managing Abiotic Stress in Agricultural Crops—A Comprehensive Review
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Srushtideep Angidi; Kartik Madankar; Muhammad Massub Tehseen; Anshika Bhatla
Abiotic stresses, such as drought, salinity, and heat, exacerbated by climate change, pose significant challenges to global agriculture. These stresses negatively impact crop physiology, leading to yield losses and complicating efforts to breed resilient varieties. While advancements in molecular biology and genomics have identified stress-resistance genes, their effective utilization in breeding programs depends on precise phenotypic evaluation under diverse stress conditions. High-throughput phenotyping (HTP) technologies have emerged as indispensable tools, enabling non-destructive, rapid assessment of critical traits like root architecture, chlorophyll content, and canopy temperature in controlled and field environments. Unlike existing reviews, this manuscript critically addresses technological barriers such as cost scalability, field adaptability, and the integration of artificial intelligence for real-time data analysis. Additionally, it provides a fresh perspective on multi-omics integration in phenomics to bridge the genotype–phenotype gap, ensuring a more holistic approach to precision agriculture. This review bridges gaps in crop improvement by identifying practical solutions to enhance the adoption of HTP in breeding programs. It ensures food security amidst the escalating impacts of climate change. © 2025 by the authors.
Classification of maize inbred lines into heterotic groups based on yield and yield attributing traits
(Cambridge University Press, 2025) Anshika Bhatla; Srushtideep Angidi; Noel Thomas; Kartik Madankar; Jai Prakash Shahi
This study investigated the combining ability, heterosis and heterotic grouping of maize (Zea mays L.) inbred lines to enhance hybrid performance and productivity. Twenty-four hybrids were developed by crossing eight inbred lines with three testers, and their performance was evaluated for two years at Banaras Hindu University’s agricultural research farm. Data on yield and yield-attributing traits were collected from selectively centred competitive plants in each row, avoiding border plants to reduce errors. Biometrical techniques, cluster analysis, and statistical tools were employed to measure general combining ability (GCA), specific combining ability (SCA), and standard heterosis, providing insights into hybrid performance. Analysis of variance revealed significant mean square values for GCA and SCA across most traits studied. Various methods were utilized, including SCA effects, HGCAMT (Heterosis Grouping by Combining Ability of Multiple Traits), and HSGCA (Heterotic Grouping based on Specific and General Combining Ability). The study identified HUZM-242 × CML-286 and HUZM-53 × CML-286 as crosses displaying higher grain yield compared to the check line DKC 7074 and exhibiting positive heterosis. The findings offer valuable guidance for maize breeding programmes by accurately identifying heterotic groups, enabling breeders to select inbred lines more likely to produce high-performing hybrids. This targeted selection reduces the number of necessary cross-breeding trials, saving time and resources. Additionally, hybrids derived from crosses between lines from different heterotic groups exhibit superior performance due to higher heterosis. These conclusions support advancements in maize breeding strategies, ultimately contributing to agricultural sustainability through increased productivity, resource efficiency, and economic benefits for farmers. © The Author(s), 2024.
Classification of maize inbred lines into heterotic groups based on yield and yield attributing traits
(Cambridge University Press, 2024) Anshika Bhatla; Srushtideep Angidi; Noel Thomas; Kartik Madankar; J.P. Shahi
This study investigated the combining ability, heterosis and heterotic grouping of maize (Zea mays L.) inbred lines to enhance hybrid performance and productivity. Twenty-four hybrids were developed by crossing eight inbred lines with three testers, and their performance was evaluated for two years at Banaras Hindu University's agricultural research farm. Data on yield and yield-Attributing traits were collected from selectively centred competitive plants in each row, avoiding border plants to reduce errors. Biometrical techniques, cluster analysis, and statistical tools were employed to measure general combining ability (GCA), specific combining ability (SCA), and standard heterosis, providing insights into hybrid performance. Analysis of variance revealed significant mean square values for GCA and SCA across most traits studied. Various methods were utilized, including SCA effects, HGCAMT (Heterosis Grouping by Combining Ability of Multiple Traits), and HSGCA (Heterotic Grouping based on Specific and General Combining Ability). The study identified HUZM-242 × CML-286 and HUZM-53 × CML-286 as crosses displaying higher grain yield compared to the check line DKC 7074 and exhibiting positive heterosis. The findings offer valuable guidance for maize breeding programmes by accurately identifying heterotic groups, enabling breeders to select inbred lines more likely to produce high-performing hybrids. This targeted selection reduces the number of necessary cross-breeding trials, saving time and resources. Additionally, hybrids derived from crosses between lines from different heterotic groups exhibit superior performance due to higher heterosis. These conclusions support advancements in maize breeding strategies, ultimately contributing to agricultural sustainability through increased productivity, resource efficiency, and economic benefits for farmers. Copyright © 2024 The Author(s), 2024.