• Jayashri Shinde
• Gitanjali Suresh Kamble
• Tripura Satish Patange
• Rushikesh Eknath Gonge
• Tejas Bhagwan Patil

Global food demand, resource scarcity, and climate change are all becoming bigger problems for agriculture.This paper presents the development of an integrated system that uses machine learning techniques to predict crop yields in line with environmental factors, namely, temperature, precipitation, soil qualities, and other climatic conditions.The collected data is based on trends that affect agricultural yields from a multitude of sources, including weather stations and remote sensors. The models used are regression analysis and random forests in machine learning. Being an instant source of information, the system empowers farmers to optimize farming techniques like fertilization and irrigation, so loss and output would be reduced. It contributes to sustainable agriculture by enabling resource-friendly methods and offering a cushioning effect against adverse environmental changes. When farmers take proactive data-driven decisions because of real-time processing capability of the system, they are better situated to take care of climatic fluctuation. This research will reveal how this kind of machine learning-based method improves the accuracy of agricultural production forecast and aids efforts toward providing global food security. It will demonstrate how the incorporation of machine learning in agriculture promotes sustainable farming methods that guarantee higher yields, which will be helpful not only to large-scale but also to small-scale agricultural enterprises. Because of its flexibility, this system could easily be scaled up for modern agriculture.

Crop yield prediction, Predictive analytics, Precision agriculture

"Advanced Yeild Prediction Models for Precision Farming using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.f500-f505, February-2025, Available :http://www.jetir.org/papers/JETIR2502570.pdf

"Advanced Yeild Prediction Models for Precision Farming using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. ppf500-f505, February-2025, Available at : http://www.jetir.org/papers/JETIR2502570.pdf