| Autor: |
Dinkar, Vishal, Sarkar, Sayantan, Pandey, Saurabh, Antre, Suresh H., Kumar, Amarjeet, Thribhuvan, R., Singh, Ashutosh, Singh, Ashish Kumar, Singh, Badal, Ahmad, Md. Afjal |
| Zdroj: |
Advances in Agronomy; January 2024, Vol. 188 Issue: 1 p247-294, 48p |
| Abstrakt: |
Scientists aim to improve crop response under stress conditions and gain better yields in continuously changing environmental conditions. They rely on plant phenotyping to quantify crop response under adverse conditions to achieve this goal and select the most tolerant genotypes. Recent advances in phenotyping platforms allow dissecting of complex traits such as abiotic stress. For example, the phenotyping platform is integrated with artificial intelligence (AI) and remote sensing tools to provide more robust, high throughput data collections in real-time changing environments. This review will give a deep understanding of the requirement of phenomics in crop improvement under stress conditions. We have discussed different phenotyping platforms, suitable traits for phenotyping, and machine learning and AI integration with the high throughput phenotypic platform for collecting a large data set of crops under stress conditions. Overall our review will dissect the phenomics aspects of complex traits, such as biotic and abiotic stress-related traits requiring sensor advancement, high-quality imagery combined with machine learning methods, and efforts in transdisciplinary science to foster integration across disciplines and better our understanding of plant stress biology. |
| Databáze: |
Supplemental Index |
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