Crop Yield Prediction for Cereals using Machine Learning

Abstract

Agriculture is a major contributor to India's financial well-being. However, challenges like population growth and climate change impact crop production. Machine learning is vital for crop forecasting and decision-making, assisting in selecting crops and optimizing farming practices According to our analysis, the most used features are humidity, temperature, soil type, rainfall, pH, area, production and the applied algorithm is Decision Tree, Support Vector Machine (SVM), Random Forest & Gradient Boosting to cultivate. This prediction helps identify the best cereal crops based on weather conditions. In recent years, farmers have faced issues like reduced rainfall and poor soil quality, leading to crop failures. Precision farming helps adapt crop management to changing environmental conditions, promoting smart farming. The study aims to help people grow high-yield cereal crops, plan their activities, and find solutions to agricultural challenges.