Crop Yield and Price Prediction Using Multifactorial Analysis

– Agriculture plays a critical role in sustaining economies and feeding populations, especially in countries like India, where a significant portion of the workforce depends on it. However, crop yield is highly influenced by numerous environmental and soil-related factors, making prediction and decision-making challenging for farmers. This study presents a data-driven crop yield prediction and market analysis system that leverages machine learning and real-world agricultural data to provide accurate yield forecasts. Using a cleaned dataset comprising key features such as temperature, rainfall, soil nutrients (N, P, K), pH, and crop type, a regression-based predictive model was developed to estimate the yield in tons per hectare. Additionally, an economic perspective was integrated through the analysis of market price trends from regional data, aiding in crop selection based on both productivity and profitability. The proposed system, supported by an interactive interface, empowers stakeholders to make informed agricultural decisions, thereby enhancing productivity and economic returns. The experimental results demonstrate the effectiveness and potential of the model to support smart agriculture initiatives.