Integrated Machine Learning Models for Cotton Crop : Soil Density Analysis, Health Monitoring and Region Based Price Prediction

Abstract

Cotton plays a major role in India’s agricultural sector; however, farmers continue to struggle with unpredictable soil conditions, changing weather patterns, and volatile market prices. These factors directly influence the yield and profit. This study developed a machine learning framework to support farmers in making informed, data-driven decisions. The system includes two components: an Agro-Environmental Model, which evaluates soil bulk density, fertility, and vegetation health using NDVI and IMD weather data and a Market Model, which predicts regional cotton prices from APMC datasets. The outputs from both models were integrated into a unified recommendation system that indicates where cotton can be cultivated efficiently, evaluates crop health, and anticipates market value. Early testing produced encouraging results, showing that the system can assist farmers in better planning their cultivation and marketing strategies.