TrioConvTomatoNet: A robust CNN architecture for fast and accurate tomato leaf disease classification for real time application

Abstract

Tomato leaf diseases are a common threat to long-term tomato protection that affects many farmers worldwide. Computer-assisted technology is more prevalent for early and accurate diagnosis of tomato leaf diseases, which may reduce the likelihood that the plant will suffer further harm. The advancement of deep learning techniques has a lot of potential for accurate classification of leaf diseases through an automated feature extraction topology. By changing the network layers and their parameters in the feature extraction topology, it is possible to extract the exact features and improve the classification accuracy. In this study, TrioConvTomatoNet, novel deep convolutional neural network architecture, is proposed, which includes a 3-series convolution layer under each stage of the feature extraction topology to capture and integrate the information from the leaf images efficiently. To improve the network's learning ability and perform accurate classifications, the proposed method incorporates the stochastic gradient descent optimizer. In order to facilitate proper learning, a separate dataset preparation strategy was followed for tomato leaf image dataset collection, which includes existing and real-time images. This research is carried out with varying the number of convolution layers to express the effectiveness of the proposed method in capturing and integrating information from the leaf images. Also, the extensive experiments on an unseen dataset collection with and without augmentation express the superiority and robustness of TrioConvTomatoNet in terms of both accuracy and speed when compared to state-of-the-art methods. The proposed method achieves remarkable accuracy of about 99.39% in disease classification while processing both existing database and real-time images, making it suitable for practical deployment in agricultural settings.