• Sham Dilip Shelke
• Abhay Sanjay Bharane
• Tejaswini Anwane
• Vaishnavi Thakare
• Dr.Z I Khan

Artificial Intelligence (AI) is transforming agriculture by enabling smarter, data-driven decisions. In this research, AI models are developed to recommend optimal crop types and precise fertilizer application strategies. By analyzing diverse agricultural data, such as soil health, weather patterns, and crop characteristics, machine learning algorithms can provide tailored recommendations for farmers. These models integrate multi-source datasets, including historical cultivation records and real-time environmental inputs, to enhance decision-making accuracy. Compared to traditional farming methods, AI-driven approaches significantly improve efficiency and sustainability. Fertilizer usage is optimized, reducing both environmental impact and input costs for farmers. The system dynamically adjusts fertilizer recommendations based on evolving soil and climate conditions. This minimizes the risks of over-fertilization and undernourishment of crops. By aligning fertilizer type and quantity with specific crop needs, farmers achieve better productivity without excessive resource usage. The proposed model supports precision agriculture practices, ensuring long-term soil health and ecological balance. It empowers farmers with actionable insights through intuitive digital platforms. Additionally, this approach contributes to reducing greenhouse gas emissions associated with fertilizer misuse. The findings demonstrate the potential of AI to revolutionize agricultural practices, promoting sustainable food production systems. This research represents a significant step towards data-driven, environment-friendly farming solutions

Crop Prediction, Fertilizer Prediction, Random Forest, Machine Learning (ML)

"AI-Driven Crop and Fertilizer Prediction System ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.m568-m573, April-2025, Available :http://www.jetir.org/papers/JETIR2504C79.pdf

"AI-Driven Crop and Fertilizer Prediction System ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppm568-m573, April-2025, Available at : http://www.jetir.org/papers/JETIR2504C79.pdf