AgriSahayak: AI-Based Smart Agriculture Management Dashboard for Farming Operations

Abstract

The rapid growth of digital agriculture platforms, smart farming technologies, and web-based agricultural systems has significantly increased the need for intelligent agricultural monitoring and farm management solutions. Modern farming environments continuously face challenges such as unpredictable weather conditions, improper irrigation management, soil degradation, pest attacks, fertilizer misuse, and fluctuating market prices. Traditional agriculture management systems often struggle to efficiently handle large volumes of farming data, monitor crop conditions accurately, and provide real-time agricultural insights for effective decision-making. In large-scale agricultural environments, manual monitoring and analysis become time-consuming, inefficient, and less reliable. This paper presents the design and implementation of an AIbased Smart Agriculture Management Dashboard aimed at improving agricultural analysis, crop monitoring, and farming decision-making in modern agricultural environments. The proposed system integrates multiple smart farming functionalities including real-time weather monitoring, crop recommendation systems, fertilizer calculation modules, mandi price analytics, yield forecasting, and intelligent agricultural insights within a centralized dashboard platform. The system uses intelligent data processing and context-aware recommendation mechanisms to support efficient agricultural planning and resource management. The dashboard provides real-time visibility into farming activities through an interactive and user-friendly interface supporting weather visualization, crop tracking, AI-assisted recommendations, soil and fertilizer analysis, market price monitoring, multilingual accessibility, role-based access control, and centralized farm management workflows. Interactive graphs, analytical charts, and visualization modules improve operational monitoring and enable farmers to make faster and more accurate agricultural decisions. The system is developed using modern MERN Stack technologies including MongoDB, Express.js, React.js, Node.js, and Tailwind CSS to ensure scalability, modularity, responsive dashboard visualization, secure API communication, and efficient data management. The modular architecture of the system also supports future enhancements such as IoT integration, machine learning-based crop prediction, cloud deployment, and real-time agricultural analytics. The proposed Smart Agriculture Management Dashboard improves agricultural productivity, operational efficiency, resource optimization, and centralized farm monitoring compared to traditional agriculture management approaches, making it suitable for modern smart farming environments.