01-Klasifikasi Daun Jagung menggunakan Convolutional Neural Network-VGG16-Tedi Maulana

Abstract

Corn is the second most important food crop in Indonesia after rice, serving
as a strategic source of carbohydrates, animal feed, and industrial raw materials.
Its productivity is often disrupted by pests and diseases, which can cause significant
losses for farmers. Technology-based solutions are needed to detect diseases early.
This study developed a corn leaf disease detection system using the Convolutional
Neural Network (CNN) method with the Visual Geometry Group 16 architecture
through a digital image processing approach. The corn leaf image dataset was
classified into several disease categories. The VGG16 model achieved an accuracy
rate of 93%, a precision value of 90%, a recall of 92%, and an F1-score of 91% in
the classification process. The results of this study are expected to help farmers
detect diseases early and improve the effectiveness of sustainable pest control