This study examines the impact of Artificial Intelligence (AI) on agriculture in India by analyzing how AI-driven technologies influence crop productivity and farming efficiency. The research focuses on AI applications such as NDVI monitoring, smart irrigation systems, rainfall forecasting, soil analysis, and crop disease detection. Using secondary data obtained from a Kaggle dataset containing over 190 observations from different Indian agricultural regions, the study applies statistical tools, including regression analysis and t-tests, to evaluate the relationship between AI-related variables and crop yield. The findings reveal that AI technologies significantly improve agricultural productivity, with NDVI, rainfall, and smart irrigation showing strong positive effects on crop yield, while temperature variations, crop diseases, and improper soil pH negatively affect productivity. The regression model demonstrated a strong fit, explaining approximately 88% of the variation in crop yield. The study concludes that AI has substantial potential to transform Indian agriculture by enabling data-driven decision-making, improving resource utilization, and supporting sustainable farming practices.

However, successful adoption requires farmer awareness, training, affordable AI tools, and integration of AI with traditional agricultural practices.

Keywords: Artificial Intelligence (AI), Indian Agriculture, Crop Yield Prediction, Smart Irrigation, Precision Farming.