Klasifikasi Penyakit Daun Sawi Menggunakan VGG19 Berbasis Citra Digital

Abstract

Agricultural productivity is greatly influenced by plant health, including mustard greens (Brassica rapa), which are prone to leaf diseases and have high economic value. This study aims to develop a digital image-based classification system for mustard leaf diseases using a deep learning approach, particularly the Convolutional Neural Network (CNN) VGG19 architecture, and to compare its performance with ResNet50 and VGG16 models. The dataset used consists of 999 images divided into two classes: healthy mustard leaves and diseased mustard leaves. The images were processed through preprocessing steps (resized to 224×224 and normalized), then split into training, validation, and testing sets (80:10:10). The VGG19 architecture was customized with additional layers such as Global Average Pooling and Dense layers, and trained for 50 epochs with a configuration of 32 filters, a dropout rate of 0.5, and a learning rate of 0.0003. The results showed that the VGG19 model achieved the highest validation accuracy of 96%, followed by VGG16 with 95%, and ResNet50 with 74%. Evaluation using a confusion matrix demonstrated that VGG19 exhibited the most stable and accurate classification performance in recognizing both classes. These findings reinforce the potential of VGG19 for developing automated and real-time plant disease detection systems. Furthermore, this study opens up opportunities for integration into agricultural Internet of Things (IoT) systems for continuous plant health monitoring, thereby assisting farmers in making faster and more accurate preventive decisions.