Klasifikasi Penyakit Daun Tomat Menggunakan Convolutional Neural Network (CNN) Berbasis EfficientNetB0

Abstract

This research is designed to develop a disease classification system on tomato leaves using the Deep Learning method with a Convolutional Neural Network (CNN) architecture based on the EfficientNetB0 model architecture with a Transfer Learning approach. The dataset used for the research was taken from Kaggle (ashishmotwani/tomato) and consists of more than 20,000 images of tomato leaves divided into 11 classes, namely 10 classes of diseased leaves and 1 class of healthy leaves. The research was conducted using the Google Colab platform with a T4 GPU. The research stages include dataset preprocessing, dataset augmentation, train-validation-test data sharing, building an EfficientNetB0-based CNN model, model training with a fine-tuning mechanism, and performance evaluation using a confusion matrix, precision, recall, and F1-score. The configuration uses an input size of 224 × 224 pixels, a batch size of 32, an initial learning rate of 0.001 and a maximum of 50 epochs. The results show that the best training accuracy of all 50 epochs was 99.63% and the best validation accuracy was 87.27%, achieved in the 49th epoch. The best model was restored from the 49th epoch based on the highest val accuracy value. The classes Powdery Mildew, Target Spot, and Tomato Yellow Leaf Curl Virus obtained the highest recall values. The implementation of EfficientNetB0 has proven effective in classifying tomato leaf diseases, making it suitable for application in mobile and web-based smart agriculture systems.