Gothe, R M and Karrem, A and Gowda, R S R and Onkarappa, D and Jaba, J and Ahn, S and Pathour, S and Yogendra, K and Bheemanahalli, R (2024) Decoding plant defense: accelerating insect pest resistance with omics and high-throughput phenotyping. Plant Physiology Reports, 29. pp. 793-807. ISSN 2662-253X
Full text not available from this repository. (Request a copy)Abstract
Genotype screening techniques in crop protection are being revolutionized by integrating multi-omics into high-throughput phenotyping (HTP). This comprehensively explains the biochemical and molecular resistance mechanisms underlying plant–insect interactions. Metabolomics offers insights into the metabolic changes and pathways activated in plants in response to insect damage, while proteomics reveals the dynamic protein expressions and modifications involved in plant defense. Quantitative measurements of unstructured/image-based and semi-structured data require sophisticated storage, processing, and advanced analysis methods. Machine learning (ML) and artificial intelligence (AI) are crucial in this integrated approach, enabling the automated, accurate, and efficient analysis of large datasets. Robust ML models can predict plant resistance levels by analyzing metabolic and proteomic profiles, while deep learning techniques can identify patterns and correlations within complex datasets. Innovations in ML models are needed to account for multiple stress factors simultaneously, reflecting real-field conditions more accurately. Utilizing advanced imaging platforms, sensor technologies, and AI-driven data analysis promises significant advancements in understanding and enhancing plant resistance to insect pests, ultimately contributing to sustainable agriculture and food security. This review provides the significance of interdisciplinary approaches in discovering specific biomarkers and pathways relevant to plant resistance against insect pests.
| Item Type: | Article |
|---|---|
| Divisions: | Global Research Program - Accelerated Crop Improvement |
| CRP: | UNSPECIFIED |
| Uncontrolled Keywords: | Artificial intelligence, High throughput phenotyping, Machine learning, Metabolomics, Proteomics, Plant resistance |
| Subjects: | Others > Genetic Engineering |
| Depositing User: | Mr Nagaraju T |
| Date Deposited: | 18 Feb 2025 04:59 |
| Last Modified: | 18 Feb 2025 04:59 |
| URI: | http://oar.icrisat.org/id/eprint/12966 |
| Official URL: | https://link.springer.com/article/10.1007/s40502-0... |
| Projects: | UNSPECIFIED |
| Funders: | Indian Council of Agricultural Research, Mississippi Agricultural and Forestry Experiment Station, USDA-Agricultural Research Service, National Institute of Food and Agriculture |
| Acknowledgement: | JJ and KY acknowledge support from the Indian Council of Agriculture Research (ICAR) core funding to ICRISAT. RB thanks the funding support of the Mississippi Agricultural and Forestry Experiment Station, the USDA-Agricultural Research Service (USDA-ARS) (58-6064-3-007), and the National Institute of Food and Agriculture (MIS 430030). |
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