Agrigenius: The Ultimate Smart Farming Web Application

Mr. Kumar K1, Tejas B R2
1Associate Professor, Department of Computer Science and Engineering, KSIT, India

2Student, Department of Computer Science and Engineering, KSIT,

Abstract:
Modern agriculture requires intelligent and automated systems to improve crop productivity, reduce manual effort, and optimize resource utilization. This paper presents “AgriGenius,” an IoT and Artificial Intelligence-based smart farming monitoring system developed using ESP32 microcontrollers, environmental sensors, ESP32-CAM, Flask web application, and YOLOv5 deep learning model. The proposed system continuously monitors important agricultural parameters such as temperature, humidity, soil moisture, and soil pH using IoT sensors. The collected data is transmitted wirelessly to a Flask-based web dashboard for real-time monitoring and analysis.

The system also integrates ESP32-CAM for live crop monitoring and AI-powered disease detection. Real-time crop images and video streams are analyzed using the YOLOv5 deep learning model to identify plant diseases, leaf abnormalities, fungal infections, and pest attacks. The proposed framework enables remote agricultural monitoring, intelligent crop analysis, efficient irrigation support, and early disease detection. Experimental results demonstrate that the system effectively supports precision agriculture by improving farming efficiency, reducing crop loss, and minimizing manual monitoring efforts. The developed solution is scalable, affordable, and suitable for both small-scale and large-scale agricultural applications.

Keywords:
Smart Farming, Internet of Things (IoT), Artificial Intelligence, YOLOv5, ESP32, Precision Agriculture, Plant Disease Detection, Flask Web Application.