Prediction of Agricultural Crop Production in India - Dr. Sandhya,; Abhishek Yadav,; Akansha Srivastava,; Ayush Yadav,; Himansh Anand, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
AI-powered prediction systems for agricultural crop production utilize advanced technologies like machine learning, deep learning, and data analytics to transform traditional farming practices. By integrating diverse datasets such as weather data, soil health, satellite imagery, and market trends, these systems enable precise yield forecasting, risk assessment, and resource optimization. This paper explores the fundamental principles, methodologies, and datasets underpinning AI-based crop prediction. It also delves into the role of machine learning models and their ability to provide actionable insights to farmers through real-time monitoring and decision support systems. Furthermore, it highlights the challenges, ethical considerations, and opportunities for scaling these systems to ensure sustainable agricultural practices and food security. The findings demonstrate that AI-driven approaches hold significant potential for revolutionizing the agriculture sector, particularly in regions like India, where agriculture plays a pivotal socio-economic role.
Keywords: Crop Prediction; Machine Learning; Artificial Intelligence; Agriculture Sector