KrishiSmart: AI-Powered Precision Agriculture & Crop Intelligence Platform

Smallholder and commercial farmers face recurring, catastrophic harvest losses—frequently exceeding 30–40% of seasonal yield—stemming from undetected crop diseases, delayed pest identification, and uncalibrated irrigation cycles. Traditional agrarian support systems depend on overburdened public agricultural extension officers who cannot provide timely, individualized guidance during early-stage pathogen outbreaks. Furthermore, severe price opacity across decentralized regional agricultural produce market committees (mandis) forces farmers to sell produce without market visibility, severely degrading operating margins and economic viability. From a software architecture standpoint, modern agricultural software must function under low-bandwidth, intermittent rural network conditions while synthesizing heterogeneous, time-critical data streams. Processing continuous meteorological telemetry, live commodity auction indices, and high-resolution convolutional computer vision inference requires an orchestrated, fault-tolerant microservices architecture capable of sub-second execution, proactive data caching, and native bilingual accessibility for non-technical agricultural operators.

ARdevlopers architected and delivered KrishiSmart—an enterprise-grade precision agriculture platform pairing a modern React 18 single-page application with a decoupled Node.js/Express API gateway and a dedicated Python FastAPI deep learning inference microservice. For plant pathology, the platform implements transfer-learning models (MobileNetV3Small and EfficientNetV2B0) fine-tuned on crop leaf datasets with Test-Time Augmentation (TTA), providing farmers with immediate disease classification, confidence scores, and dual organic/chemical treatment protocols directly from mobile photo uploads. The system integrates real-time WeatherUnion meteorological telemetry and soil condition vectors into an automated rule-based agronomic engine with a multi-tiered MongoDB TTL cache (6-hour expiration), guaranteeing instant sub-120ms advisory retrieval. The client interface is built with Vite, TypeScript, Tailwind CSS, and Radix UI/Shadcn primitives, localized in English and Hindi via i18next. Live Agmarknet mandi commodity feeds are rendered through Recharts with 7, 14, and 30-day historical trend analytics and intelligent selling recommendations, backed by Socket.io event broadcasting and robust defense-in-depth security.