Identifikasi Penyakit pada Daun Jagung dengan Menggunakan Metode Convolutional Neural Network (CNN)

Abstract

One of the major problems that need to be addressed is the identification of infection on corn leaves. The quality and production of crops can decrease due to the presence of diseases on corn leaves. The process of diagnosing diseases on corn leaves manually takes time and effort. Therefore, a more effective and accurate technique is needed to know the existence of diseases on corn leaves. In this study, Convolution Neural Network (CNN) is used as a technique for identifying diseases on corn leaves. A machine learning model called CNN can be used to identify characteristics in images. To train CNN, a dataset of corn leaf images labeled with the names of predetermined corn leaf diseases is used. The accuracy rate that has been achieved is 95%, with a precision of around 95%. The recall rate also reaches 95%, while the F1-score reaches 95%.This method of identifying diseases on corn leaves using CNN can be used to improve the efficiency and accuracy of disease identification on corn leaves. This method can also be used to develop an early warning system for diseases on corn leaves.