Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection

Tomatoes are one of the world's greatest valuable vegetables and are regarded as the economic pillar of numerous countries. Nevertheless, these harvests remain susceptible to a variety of illnesses which can reduce and destroy the generation of healthy crops, making early and precise identification of these diseases critical. Therefore, in recent years, numerous studies have utilized deep learning (DL) models for automatic tomato leaf illness identification. However, many of these methods are based on a single DL architecture that needs a high computational ability to update these hyperparameters leading to a rise in the classification complexity. In addition, they extracted large dimensions from these networks which added to the classification complication. Therefore, this study proposes a pipeline for the automatic identification of tomato leaf diseases utilizing three compact convolutional neural networks (CNNs). It employs transfer learning to retrieve deep features out of the final fully connected layer of the CNNs for more condensed and high-level representation. Next, it merges features from the three CNNs to benefit from every CNN structure. Subsequently, it applies a hybrid feature selection approach to select and generate a comprehensive feature set of lower dimensions. Six classifiers are utilized in the tomato leaf illnesses identification procedure. The results indicate that the K-nearest neighbor and support vector machine have attained the highest accuracy of 99.92% and 99.90% using 22 and 24 features only. The experimental results of the proposed pipeline are also compared with previous research studies for tomato leaf diseases classification which verified its competing capacity.