New Tomato Leaf Disease Classification Method Based on DenseNet121 with Bat Algorithm Hyperparameters Optimization

Tomatoes are recognized for their nutritional value in disease prevention, yet foliar infections often hinder their production. Monitoring and management of tomato leaf disease need much time, cost, and effort. Artificial Intelligence (AI), specifically Deep Neural Networks offers a promising solution for automating disease detection, such as Convolutional Neural Networks (CNN). CNN has several advantages over the traditional machine learning. Selecting the optimal CNN architecture is crucial for accurate feature extraction from image data. Numerous well-known CNN-based architectures have been proposed and used in numerous studies, including VGGNet and DenseNet. DenseNet121 has been proven to produce high classification accuracy. However, the DenseNet121 performance depends on the selection of optimal hyperparameter. This paper proposes an enhancing DenseNet121 performance through hyperparameter optimization using the Bat Algorithm (BA) for classifying tomato leaf disease. BA is used in this research since it has good performance and some advantages. The proposed method achieves better performance than the original DenseNet121 and has a competitive computational time. It produces an accuracy of 0.9465, macro-average precision of 0.9498, macro-average recall of 0.9465, and macro-average f1-score of 0.9460. This method is prospective to be implemented in devices in the real problem with many various image conditions in the future.