International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Agricultural productivity is increasingly affected by unpredictable weather conditions, plant diseases, inefficient irrigation practices, and limited access to timely market and advisory information. These challenges are more prominent among small and medium-scale farmers who rely on traditional decision-making methods. To address these issues, this research presents an AI-Based Precision Agriculture Support System that provides comprehensive and intelligent assistance for modern farming. The proposed system integrates multiple analytical modules, including crop recommendation, crop yield prediction, plant disease detection, irrigation planning, market price analysis, and weather forecasting. Machine learning algorithms are employed to analyze soil characteristics, climatic parameters, historical crop data, and market trends, while deep learning techniques are used to identify plant diseases from leaf images at an early stage. The irrigation planning module utilizes predictive insights combined with weather forecasts to optimize water usage and reduce resource wastage. All modules are unified through a web-based platform that delivers real-time, user-friendly recommendations without dependence on complex sensing infrastructure. Experimental results demonstrate that the system produces reliable predictions and actionable insights, contributing to improved crop management, efficient resource utilization, and enhanced decision-making. The proposed solution offers a scalable, cost-effective, and sustainable approach toward intelligent agriculture and supports the adoption of data-driven farming practices.

Keywords: Precision Agriculture, Artificial Intelligence, Crop Recommendation, Yield Prediction, Plant Disease Detection, Smart Irrigation, Market Price Analysis, Weather Forecasting.

Downloads: | DOI: 10.17148/IJARCCE.2026.151122