Weather-Based Crop Yield Prediction

Agriculture plays a critical role in India’s economy, providing livelihoods to millions of people and contributing significantly to the nation’s GDP. In fact, agriculture is able to support 45% of India’s employed labor force. However, Indian farmers face numerous challenges, including unpredictable weather which leads to poor yields, often leading to financial instability, exacerbating poverty and rural distress. Predicting crop yields using machine learning models offers a promising solution to this problem. The model this paper proposes leverages meteorological data (temperature, rainfall, etc.) as well as farming practice data (use of pesticide, fertilizer etc.) to help farmers predict their yield. The model presented in this paper ultimately had a mean squared error of 4.16 and a correlation value of 0.761 when predicting yields.