Crop Survival and Price Prediction

The Crop Survival model is a machine learning tool that analyses market trends and predicts crop viability in specific regions based on historical data from the past eight years. It leverages diverse variables, including weather patterns, soil quality, irrigation methods, pest infestation rates, and socioeconomic indicators, to forecast which crops are most likely to thrive in particular areas and seasons. The model adapts to changing agricultural conditions, incorporating new data and refining its algorithms to remain effective despite unpredictable factors like climate change, economic fluctuations, and technological advancements. This adaptability allows it to provide accurate, real-time predictions that evolve as conditions change. Beyond helping farmers make informed crop selections, the model is also valuable for policymakers, agricultural researchers, and market analysts. It provides comprehensive insights into crop survival probabilities and market dynamics, supporting strategic planning, resource allocation, and risk management in the agricultural sector, ultimately improving decision-making and agricultural practices. Key Words: Machine Learning, Crop Prediction, Decision Tree, SVM, Rainfall Prediction, Crop Recommendation.