AGROVAAI-Powered Precision Farming

Abstract

India is home to approximately 140 million farmers, yet an estimated 70% of them lack access to real-time, actionable data about their land, crops, and market conditions. This information gap contributes to an annual crop loss estimated at ₹50,000 crore. Compounding the problem, nearly 70% of rural India experiences poor or unreliable internet connectivity, rendering most existing "smart farming" solutions — which depend on cloud connectivity and mobile applications — impractical for the very population they are meant to serve. This thesis presents AGROVA, a low-cost, fully offline, AI-powered precision farming device built around an ESP32 microcontroller and a suite of five environmental sensors: soil moisture, sunlight (LDR), temperature, rainfall, and gas/methane (MQ-4). Sensor readings are processed by an onboard eight-factor AI scoring engine — spanning soil chemistry, weather fitment, market economics, crop rotation, pest and disease risk, water availability, subsidy alignment, and seasonal match — to rank 50 candidate crops, present the top 10 recommendations, generate a risk and loss alert, and estimate return on investment, all without an internet connection. AGROVA introduces what is, to the author's knowledge, the first gas safety alert integrated into any farm advisory AI system, detecting methane accumulation from manure pits and triggering an audible alarm, a visual red alert, and a documented emergency protocol. The device renders all output in 22 official Indian languages using a custom embedded Devanagari bitmap font engine that operates entirely offline, and it serves a companion web application from its own Wi-Fi hotspot so that a farmer's phone requires zero mobile data to access photo-based crop analysis and language switching. The complete Bill of Materials totals ₹1,487, keeping the device under the ₹1,500 target price point. Testing indicates a crop recommendation accuracy of 90–93%, validated against ICAR reference data, alongside projected outcomes of 20% higher yield and 30% water savings. This thesis documents the problem context, system architecture, AI methodology, logistics and deployment framework, and results of the AGROVA prototype, and outlines a three-phase roadmap toward a sensor mesh, satellite-linked advisory system, and nationwide deployment to 50 million farmers. Keywords: precision agriculture, edge AI, offline IoT, ESP32, rural logistics, crop advisory systems, methane safety alert, multilingual embedded systems, PMFBY, agricultural technology for smallholders