KisanVyapar: A Digital Farm-to-Market Platform with Multilingual AI Advisory for Indian Smallholder Farmers
TEJAS SHELAR / VISHWAJIT GARJE / ARHAN SAYYED
School of Engineering
Ajeenkya DY Patil University, Lohegaon, Pune, India
Abstract
India's agricultural economy is burdened by a fragmented supply chain in which smallholder farmers—constituting more than 86% of total farm holdings—are consistently denied fair market prices due to multi-tiered intermediary networks that collectively absorb 30–50% of the final consumer price. This paper presents KisanVyapar, a full-stack, web-based farm-to-market digital platform designed to eliminate intermediary dependency. The system provides a role-differentiated dual-user environment for farmers and merchants, incorporating a dynamic product marketplace with category filtering and image upload, a lifecycle-managed order processing system, a real-time peer-to-peer messaging module, and KisanMitra—an AI-powered agricultural advisory chatbot powered by the Claude AI API (Anthropic). KisanMitra detects and responds in English, Hindi (हिन्दी), and Marathi (मराठी) automatically, providing expert guidance on crop diseases, fertilizer selection, mandi price estimation, irrigation scheduling, and government scheme navigation. The entire platform UI is fully internationalized across the same three languages via a server-side PHP translation layer. Implemented on a LAMP stack using PHP 8.x and MySQL 8.0, the platform is deployable on standard shared hosting infrastructure. Evaluation through structured functional testing and expert chatbot assessment confirmed: average module response times of 1.2–2.1 seconds, chatbot domain accuracy of 91.2% (English), 88.8% (Hindi), and 87.6% (Marathi), and zero critical failures across all 42 user-role workflow test cases.
Keywords — agricultural e-commerce, farm-to-market platform, AI chatbot, multilingual NLP, rural digitization, PHP web application, smallholder farmers, KisanMitra, Claude AI API, supply chain disintermediation
