MICROBIAL INSIGHTS: LEVERAGING SOIL HEALTH FOR PREDICTIVE CROP ANALYTICS

Agricultural productivity is directly influenced by soil health, which depends on a combination of biological, chemical, and environmental factors. Conventional soil analysis methods mainly focus on nutrient composition and often overlook biological indicators such as microbial activity, resulting in limited accuracy in crop yield estimation. This project presents a machine learning–based framework for predictive crop analytics by leveraging soil health parameters, with particular emphasis on microbial indicators. The proposed system analyzes soil characteristics including nutrient levels, organic carbon, microbial biomass, soil pH, and environmental factors to predict crop yield accurately. Supervised learning models such as Random Forest and XGBoost are employed, and an ensemble learning approach is used to enhance prediction reliability. A user-friendly interface enables users to input soil test values and obtain predicted yield results. Experimental evaluation demonstrates that integrating microbial insights with machine learning significantly improves prediction accuracy and supports sustainable agricultural decision-making.

Keywords: Soil Health, Crop Yield Prediction, Microbial Analysis, Machine Learning, Ensemble Learning, Precision Agriculture