AgriCopilot: An Offline-Capable, Edge-Native Single-Page Architecture for Precision Agronomy and Inter-Market Price Routing

Abstract: Smallholder farmers in developing regions face compounding challenges: unreliable soil-crop align-ment, non-transparent advisory systems, post-harvest price collapses caused by seasonal overproduction, and cycle-bound high-interest debt. Existing agri-tech solutions often function in silos, depending heavily on high-cost IoT hardware or continuous cloud connectivity while lacking local language support. This study presents AgriCopilot, an offline-first Single-Page Application (SPA) designed to run edge-native Explainable AI (XAI) directly within client web browsers. Operating through HTML5 Canvas memory buffers, the system executes an image-based soil classification pipeline in under 50 ms without external server calls. The model computes a 9-dimensional color-texture feature vector—comprising CIE- L∗a∗b∗ coordinates, hematite redness metrics, or-ganic carbon indicators, Laplacian roughness, and normalized chromaticities—to categorize soils across six major Indian taxonomic orders. A biophysical stress engine applies non-linear penalties for temperature and precipita-tion anomalies using physiological thresholds established by the Indian Council of Agricultural Research (ICAR), generating a composite Plant Health Score and flagging visual soil mismatches. When critical stress is detected, the engine ranks climate-resilient alternative crops and updates system variables with single-tap interaction. To mitigate localized price drops, a supply-elasticity model detects regional sowing saturation (> 80%), while a geospatial routing engine evaluates road winding factors and freight costs to identify profitable interstate termi-nal markets. Finally, the platform categorizes financial support options (e.g., the 4.0% Kisan Credit Card vs. the Agriculture Infrastructure Fund) based on landholding threshold criteria, delivering explanations through client-side voice synthesis across six regional languages.

Keywords: Explainable Artificial Intelligence (XAI); Precision Agronomy; Edge Computing;Soil Image Classification; APMC Mandi Price Elasticity; Supply-Induced Price Collapse; Inter-Market Price Routing; Targeted Credit Allocation.