AI Approach for Resource Optimization in Smart Agricultural System

Smart agriculture aims to enhance crop productivity and resource utilization through intelligent monitoring and automated decision-making systems.This paper presents an AI-based resource optimization system that integrates computer vision, real-time soil analysis, and IoT automation to support precision farming.A Convolutional Neural Network (CNN) model is used to classify crop health from live camera images, identifying crops as healthy or unhealthy.Soil moisture and NPK (Nitrogen, Phosphorus, Potassium) levels are continuously monitored using IoT sensors connected to a Raspberry Pi.Based on the crop condition and moisture level, the system automatically controls irrigation through a relay-driven water pump, preventing both over-watering and under-watering.Additionally, the measured NPK values are analyzed to provide AI-based fertilizer recommendations suited to the crop's nutrient deficiency.All data-including live crop images, sensor readings, pump status, and recommendations-is displayed on a Flask-based dashboard in real time.The proposed system reduces manual intervention, optimizes resource usage, and supports data-driven decision making for sustainable agriculture.