Klasifikasi Penyakit Pada Daun Apel Menggunakan Metode CNN dengan Model VGG19

The apple, which originated in West Asia, is one of the most widely consumed fruits in the world
and can be grown in subtropical climates. Apple cultivation in Indonesia began in 1934, but
apple farming often faces the challenge of diseases that cause major economic losses. The
disease appears as spots on the leaves with diverse characteristics, making it difficult to identify.
Therefore, image processing technology is needed to accurately identify the disease. This study
uses Convolutional Neural Network (CNN) with VGG19 model to classify diseases on apple
leaves. Four classes were formed: one for healthy apples and three for apple diseases (apple scab,
apple black rot, and cedar apple rust). The purpose of this study is to help early identification and
classification of apple leaf diseases for rapid disease control. The results show that the CNN
method with VGG-19 architecture can classify apple leaf diseases with 97.07% accuracy.