Published Paper Details:

This research focuses on to develop a machine learning application for aiding farmers with crop choice, disease detection, advice translation to local languages, and chatbot assistance using of computer vision and machine learning to optimize crop yields. Traditional farming practices often rely on manual labor and subjective decisions. However, recent advancements in computer vision and machine learning have allowed for the development of intelligent agricultural systems that can aid in crop monitoring, disease detection, and yield prediction. By leveraging data from sensors, drones, and satellites, these systems can analyze plant growth patterns, soil conditions, weather data, and other relevant factors to provide real-time recommendations for optimal crop management. This approach has the potential to revolutionize agriculture by increasing crop yields, reducing waste, and improving the overall sustainability of the industry.