Design and Implementation of AgroNxt: A Full-Stack Smart Farming Decision Support System with Predictive Precision Agronomy Models

Abstract

In today's highly volatile agricultural sector, providing farmers with real-time, actionable insights is critical to improving production efficiency, mitigating financial risks, and ensuring environmental sustainability. Traditional farming relying on historical guesswork struggles to adapt to accelerating environmental variations. This paper presents the complete end-to-end architecture, implementation matrix, and empirical testing results of the AgroNxt-Smart Farming Decision & Planning system. Built using a decoupled client-server architecture, the presentation tier is powered by a React and Next.js ecosystem, styled with Tailwind CSS and Radix UI components, to provide an interactive, responsive operational dashboard across mobile and desktop environments. The application automatically maps geographic location variables, baseline soil macronutrient levels (Nitrogen, Phosphorus, Potassium), and pH scores to configure optimized land utilization metrics. The backend tier is driven by a Python and FastAPI asynchronous framework that manages a persistent SQLite database in Third Normal Form (3NF) via SQLAlchemy ORM. To handle precision crop recommendation, the platform implements an Intelligence Tier utilizing a pre-trained Scikit-Learn Random Forest Classifier model, delivering suitability lookups and localized confidence scores in under 300 ms. Additionally, the platform incorporates automated geospatial queries, client-side vector farm partitioning tools, live multi-state Mandi commodity pricing streams, a peer-to-peer farmer discussion forum, and an administrative hub integrated with official administrative welfare networks such as the PM-KISAN portal. Comprehensive performance audits validate sub150 ms session validations via JSON Web Tokens (JWT) alongside a round-trip climate telemetry synchronization delay of 340 ms, establishing the framework as a resilient, scalable digital asset management template for multi-crop enterprise agriculture.