Published Paper Details:

The proposed framework leverages multi-spectral satellite imagery to assess crop health and growth patterns across diverse agricultural landscapes. By incorporating historical yield data and real-time weather information, the AI models can identify complex relationships between environmental factors and crop productivity. This innovative approach not only enhances the precision of yield forecasts but also provides valuable insights into the impact of climate variability on agricultural output, enabling policymakers and farmers to implement adaptive strategies for sustainable food production. Agricultural productivity is inherently influenced by climatic, geographical, and management factors. India, with its diverse agro-climatic zones, requires intelligent tools to monitor crop yields, especially in its agriculturally vital northern states. Traditional methods are slow and resource-intensive, making real-time predictive systems essential. This study presents an AI-driven geo-spatial framework that leverages remote sensing and machine learning to predict seasonal crop yields.