• Shraddha Siddharam BIrajdar
• Rutuja Vaibhav Kupade
• Ayesha Karim Mujawar
• Monali Bandu Dhembare
• Prajakta Dilip Sathe

Crop prediction project utilizes machine learning algorithms to forecast crop yields based on a comprehensive analysis of historical and real-time agricultural data. By uncovering hidden patterns and relationships within factors such as weather conditions, soil characteristics, and cultivation practices, our model offers accurate predictions for future crop outcomes. This data-driven approach empowers farmers with actionable insights, enabling them to make informed decisions regarding resource allocation, risk management, and sustainable farming practices. Ultimately, this project aims to enhance agricultural productivity, optimize resource usage, and contribute to global food security in an increasingly dynamic and challenging environment.

Crop prediction, Machine learning, Agriculture, Predictive modelling, Crop yield estimation, Time-series analysis, Classification algorithms, Regression algorithms, Supervised learning, Unsupervised learning, Agricultural sustainability

"Agri Forecast Crop Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.o312-o317, April-2024, Available :http://www.jetir.org/papers/JETIR2404F46.pdf

"Agri Forecast Crop Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppo312-o317, April-2024, Available at : http://www.jetir.org/papers/JETIR2404F46.pdf