Decoding plant defense: accelerating insect pest resistance with omics and high-throughput phenotyping

ORCID

Bheemanahalli: https://orcid.org/0000-0002-9325-4901

MSU Affiliation

College of Agriculture and Life Sciences; Department of Plant and Soil Sciences; Department of Biochemistry, Nutrition, and Health Promotion

Creation Date

2025-11-19

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.

Publication Date

12-1-2024

Publication Title

Plant Physiology Reports

Publisher

Springer

First Page

793

Last Page

807

Link to Full Text

Share

COinS
 

Digital Object Identifier (DOI)

https://doi.org/10.1007/s40502-024-00835-y