Enhanced Tomato Disease Classification Using Hybrid Deep Learning Approach

Abstract

Tomato disease classification is important for agricultural productivity and food security, yet achieving high accuracy stays challenging due to the complexity of disease symptoms. This study proposed a model classification utilizing DenseNet121 combined with an Autoencoder for feature extraction and Simple neural network (SNN) for classifier. The dataset contains 10 classes of tomato diseases. Experimental results shows that the proposed approach achieved a high accuracy of 99%, outperforming the performance of several approaches. The integration of the Autoencoder enhances feature extraction and dimensionality reduction, important for improved classification outcomes. This study highlights the efficacy of the proposed work in achieving state-of-the-art accuracy in tomato disease detection. Future work may focus on refining the model using advanced augmentation techniques and expanding the dataset to enhance its applicability across diverse agricultural conditions.