AgriChain: A Farmer-to-Market Intelligence Platform Integrating Satellite Imagery, Deep Learning, and Market Analytics for Precision Agriculture

Smallholder farmers in developing agricultural economies continue to face significant losses arising from delayed detection of crop stress, undiagnosed plant diseases, and volatile, opaque market pricing. This paper presents AgriChain, an endto-end farmer-to-market intelligence platform that unifies remote-sensing analytics, deep-learning-based disease diagnosis, and data-driven market forecasting within a single accessible interface. The system ingests multispectral Sentinel-2 imagery through the Google Earth Engine API to compute the Normalized Difference Vegetation Index (NDVI) for continuous crop-health monitoring, while a convolutional neural network trained on annotated leaf-image datasets performs on-demand disease classification from farmer-submitted photographs. A Long Short-Term Memory (LSTM) network forecasts short-term commodity prices from historical mandi (market) records, and a rule-augmented recommendation engine fuses the outputs of these three subsystems to generate actionable, field-specific guidance covering irrigation scheduling, treatment recommendations, and optimal selling windows. The platform is implemented using a React.js frontend, a Node.js/Express backend, and a PostgreSQL/PostGIS spatial database. Experimental evaluation on a held-out test set of 4,120 leaf images across six disease categories yields a classification accuracy of 94.6%, while the price-forecasting module achieves a mean absolute percentage error of 6.8% over a seven-day horizon. Comparative analysis against three existing approaches demonstrates measurable improvements in detection accuracy and decision latency. The results indicate that tightly coupling satellite-derived agronomic indicators with deep learning and market intelligence can meaningfully narrow the information gap between farm-level decisions and market outcomes.