AgriYield is a simple machine learning-based project designed to predict crop yields using climate factors like rainfall, temperature, and pesticide usage. It helps farmers and researchers make informed decisions to improve agricultural productivity.
This project analyzes how climate factors such as rainfall, temperature, and pesticide usage influence crop production. By training ML models on this data, it helps provide better insights into agricultural planning.
- Crop yield prediction using real climate data
- Focus on major crops: Wheat, Maize, Rice, and Potatoes
- Data preprocessing and visualization included
- Model training using Random Forest Regressor
- Evaluation of model performance with metrics
- Python
- Pandas, NumPy
- Scikit-learn
- Matplotlib, Seaborn
- Agriculture researchers and analysts
- Government policy makers
- Students and learners exploring climate impact on agriculture
- Farmers (indirectly, via decision support tools)
The aim is to assist in making data-driven agricultural decisions and highlight how climate conditions affect crop productivity in India.