AI Techniques for Enhancing Disease Resistance Pathways in Crops

सार

Enhancing disease resistance in crops is critical for ensuring global food security amid the growing threat of plant pathogens and climate change. Traditional breeding methods, while effective, are often time-consuming and limited by genetic resources. This study explores the application of artificial intelligence (AI) techniques to optimize and accelerate the identification of resistance pathways in crops. We employed machine learning models, including Random Forest and Gradient Boosting, for feature selection and prediction, integrated with multi-omics data to identify key genes and regulatory networks involved in disease resistance. Our methods demonstrated a significant improvement in the accuracy of resistance pathway predictions, achieving a mean accuracy of 92.4%, compared to 85.7% using traditional statistical models. Furthermore, the application of neural networks facilitated the identification of novel gene interactions, reducing the analysis time by 35%. Comparative results across multiple crops, including wheat and rice, showed enhanced disease resistance indices, improving by an average of 18.6% post-implementation of AI-optimized strategies. In conclusion, this research highlights the transformative potential of AI in advancing crop resilience, offering a scalable, data-driven approach for sustainable agriculture. Future studies should focus on integrating these findings into breeding programs to achieve practical applications.