Published Paper Details:

In this study, we present a novel crop production system that uses machine learning to transform agriculture. Using advanced algorithms, the system accurately predicts crop yields based on historical and real-time information, and optimizes resource allocation and farming decisions. Our goal is to improve precision agriculture by creating reliable and adaptive models that account for the various factors that influence yield variations. Crop yield prediction is one of the challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. The prediction of crop yield is based on soil, meteorological, environmental, and crop parameters. Incorporating sensor data and satellite imagery, the system will understand optimal planting strategies and irrigation programs for sustainable agriculture. Continuously improving collaboration with farmers and stakeholders ensures efficiency, while a user-friendly interface promotes widespread adoption. This innovation is an important step towards efficient, sustainable, and data-driven crop management in modern agriculture.