Klasifikasi Penyakit Pada Buah Jambu Biji Menggunakan Algoritma Yolo V5

Abstract

Horticultural agriculture, especially guava (Psidium guajava), has great economic potential in Indonesia. However, productivity often declines due to fruit disease attacks, which are still manually diagnosed by farmers. This study aims to develop an artificial intelligence-based guava disease classification system using the You Only Look Once (YOLO) version 5 algorithm. The dataset consists of 600 images divided into three disease classes: Phytophthora, Styler and Root, and Scab. Data were collected through field documentation, then preprocessed and augmented using Roboflow. The dataset was divided into 70% training data, 20% validation, and 10% testing. The YOLOv5 model was trained using Google Collaboratory and consistently evaluated using the Confusion Matrix and accuracy, precision, recall, and F1-score metrics. The test results showed that the model achieved an accuracy of more than 95% with high precision, recall, and F1-score values ​​for each disease class. This proves that YOLOv5 is effective for real-time guava disease detection. This research contributes to the application of artificial intelligence technology to help farmers make early diagnoses quickly and accurately, thereby reducing the risk of reduced crop yields.