Penggunaan YOLOv8 untuk Deteksi Penyakit Daun Kopi - Bitra, Marcelino; Dewi, Christine (Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen), 2024)
One of the products of plantation with a significant role in economic activities in Indonesia is coffee. But, coffee production in Indonesia is experienced a decline, where one of the causes is pest and disease attacks. Artificial intelligence can be a solution to help farmers detect diseases in coffee plants using object detection algorithm. This research uses the YOLOv8 object detection algorithm to carry out detection of the state and diseases of coffee plant leaves which are divided into four classifications, namely miner, rust, phoma and healthy. The research was conducted in three experimental scenarios which were differentiated based on a comparison of data distribution in the test set, validation set, and test set, where in sequence of train, validation, and test, the first scenario had a comparison of 80:10:10, the second scenario 70: 15:15, and third scenario 70:20:10. The research process using the YOLOv8s model got a model with the best performance results in data comparison of 70% train set, 20% validation set, and 10% test set. The best performing model has a mAP value of 97.8%, precision 95.2%, recall 96.6%, and f1-score 96%.