USE OF ARTIFICIAL INTELLIGENCE IN AGRICULTURAL PRODUCTION IN INDIA: SPECIAL REFERENCE TO WHEAT PRODUCTION (International Journal of Food and Nutritional Sciences, 2023)
Artificial Intelligence (AI) is revolutionizing agricultural practices worldwide, with significant implications for India’s wheat production. As one of the largest producers of wheat globally, India faces numerous challenges, including climate variability, resource limitations, and the need to increase productivity to meet the demands of a growing population. AI offers innovative solutions to these challenges by enhancing precision farming, optimizing resource management, and improving pest and disease control.This study examines the use of AI in wheat production in India, highlighting its potential to transform traditional farming practices. AI-driven technologies, such as predictive analytics and machine learning, enable farmers to make data-informed decisions, improving crop yields and reducing environmental impact. By analyzing data from satellite imagery, weather forecasts, and soil sensors, AI systems optimize irrigation, fertilizer application, and planting schedules, leading to more efficient use of resources. Additionally, AI plays a crucial role in pest and disease management. Advanced image recognition and diagnostic tools allow for early detection and targeted treatment, minimizing crop losses. AI also facilitates climate adaptation by predicting weather patterns and helping farmers choose resilient wheat varieties and adjust farming practices accordingly. However, the adoption of AI in Indian agriculture faces challenges, including data accessibility, infrastructure limitations, and the need for farmer training and support. Addressing these challenges is essential to fully realize the benefits of AI in wheat production.In conclusion, AI holds immense potential to enhance wheat production in India, contributing to greater food security, sustainability, and resilience in the face of climatic and environmental challenges. Its successful implementation could serve as a model for integrating AI into agriculture across other crops and regions.