Deep Learning Jaringan Saraf Tiruan Untuk Pemecahan Masalah Deteksi Penyakit Daun Apel

Abstract

Diseases on apple leaves are becoming a major issue for apple growers since they can cause the crop to fail. Due to the diversity of diseases that can affect apple leaves, it can be challenging for farmers to determine the cause of leaf damage. The purpose of this research is to evaluate a convolutional neural network (CNN) method for its potential use in solving the problem of apple leaf disease identification. Four types of illness are dealt with: normal, multi-illness, rusty, and scabby. Many methods, such as data preparation and a preset VGG-16 artificial neural network (CNN) architecture, are recommended for use in the deep artificial neural network processing method. The most precise outcomes occurred when the beta parameter value was set to 2 = 0.999 at Ephoch to 85/100 with an accuracy of 0.7582, and when the epsilon parameter value was set to 1e-07 at Ephoch to 32/100 with an accuracy of 0.7582 with the best accuracy.