Multilingual Rural Tech Assistant Using Geo-Location for Web-Based Farming Marketplace

Abstract—Agriculture e-commerce platforms are changing the way farmers and buyers engage by eliminating middlemen and allow- ing for direct contact. An important requirement for such platforms is efficient location-aware search, which ensures that buyers may discover the nearest farmers selling the desired commodities. This research project develops and compares three distance algorithms: the Haversine formula for spherical distance, the Euclidean method for planar approximation, and road-based distance via the OSRM/Google Directions API. The software also includes a bilingual Pesticides Sales Dashboard, which gives aggregated information on pesticide sales across stores, improving transparency and decision-making. Ex- perimental examination reveals clear trade-offs: Haversine balances accuracy and speed, Euclidean provides quick approximations for smaller radii, and road-based algorithms provide useful routing infor- mation at the expense of increased latency. The dashboard strengthens the platform by combining logistical intelligence and market insights. Together, these modules form a comprehensive solution for modern agricultural e-commerce. Keywords: agriculture e-commerce, Haver- sine formula, Euclidean distance, road distance, OSRM, dashboard analytics, internationalisation, MongoDB Geospatial.