International Journal of Multi-Disciplinary Research and Developmental Studies
E-ISSN: 2469-7811 • Impact Factor - 9.587

FUTURE POTENTIAL OF ARTIFICIAL INTELLIGENCE AND PROTEOMICS IN CONTROLLING PLANT DISEASES

Abstract

Artificial intelligence and proteomics combine to enable pre-symptomatic plant disease detection, targeted molecular treatments, and resilient crop breeding. [1, 2, 3] Future Potential of AI and Proteomics in Plant Disease Control • Pre-Symptomatic Molecular Profiling: o AI-driven proteomics analyzes protein abundance and modification states in plant tissues (such as leaves and seeds) to spot disease anomalies before physical symptoms appear. o Profiling helps evaluate seed quality and stress tolerance (e.g., managing barley yellow mosaic virus) prior to planting. [1, 2] • Early & Automated Disease Diagnostics: o Deep learning, computer vision, and machine learning models process massive biological and spectral imaging datasets to identify pathogens with high precision. o Smartphone apps and drone-mounted sensors allow real-time field surveillance, cutting down reliance on broad-spectrum chemical pesticides. [1, 2, 3, 4, 5] • Predictive Outbreak Forecasting: o Integrating climate, soil, and protein-level data via predictive analytics forecasts spatial-temporal disease risks weeks in advance. o Farmers receive early warning alerts to apply localized, precision interventions rather than blanket treatments. [1, 2, 3] • Accelerated Crop Breeding & Resistance: o AI speeds up the mapping of protein interaction networks (interactomes) and gene expressions. o This accelerates breeding programs designed to develop high-yielding crops naturally resistant to emerging pathogens and climate stress. [1, 2, 3, 4]

Keywords: artificial intelligence, proteomics, plant diseases, crops, control

Download Full Paper (PDF)