Automated Fruit Disease Detection and Smart Irrigation System Using YOLOv8

This project presents a Automated Fruit Disease Detection and Smart Irrigation System Using YOLOv8 for precision agriculture. A USB camera is used to continuously monitor fruits on the trees, and the YOLOv8 deep learning model is employed to detect and track fruit growth and maturity in real time. Along with visual monitoring, a soil moisture sensor is used to evaluate soil dryness, and a pH sensor is used to measure soil acidity or alkalinity to assess soil quality. Based on the sensor readings, the system automatically controls a DC water pump. When the soil moisture level falls below a predefined threshold, the DC pump is switched ON to provide irrigation, and it is turned OFF once optimal moisture is reached. And GSM is used for message alert. The combined use of image-based fruit monitoring and soil condition analysis enables better crop management, improved yield, and efficient water usage. This system supports intelligent decision-making in smart farming by integrating deep learning, sensor-based monitoring, and automated irrigation.