The performance comparison of pre-trained networks with the proposed lightweight convolutional neural network for disease detection in tomato leaves

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Ecemis I. N., İLHAN H. O.

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, vol.38, no.2, pp.693-705, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 2
  • Publication Date: 2023
  • Doi Number: 10.17341/gazimmfd.1003730
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Art Source, Compendex, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.693-705
  • Keywords: Tomato Leaf Disease Detection, Convolutional Neural Networks, Transfer Learning, Data Augmentation, Leaf Classification, LEARNING-BASED APPROACH, CLASSIFICATION
  • Yıldız Technical University Affiliated: Yes


Purpose: Most of the diseases in tomatoes can be determined by observing the leaves. Therefore, the observation of tomato leaves is of critical importance in vegetable cultivation. Since preserving the health of the leaves will increase the productivity of tomatoes, it is a necessity to observe the leaf development and to determine the possible disease risks accurately and quickly in advance. The main purpose of the study is to create a CNN network to automatically determine the diseases in tomatoes using the only leaf images. Theory and Methods: In the proposed study, the "T-LeafNet " Convolutional Neural Network model, which can detect 9 different diseases and healthy leaves in the leaves of tomato plants, is proposed. Classification success was measured by training the proposed network from scratch. In addition, within the scope of transfer learning, MobileNetV2, AlexNet and VGG16 architectures, which are ready-made network models, were also tested on the dataset and the results were compared. The effects of data augmentation and learning coefficient parameters on the achievement were also examined by applying the tests on the original and augmented data with different learning coefficients. Results: The highest classification performances of T-LeafNet, AlexNet, MobileNetV2 and VGG16 networks were measured as 97.32%, 98.32%, 99.1% and 99.21%. Considering the training time and classification performances, it has been observed that the proposed T-LeafNet network gives high performance with shorter time and less resources compared to other tested networks. Conclusion: Contrary to the long training times and high resource usage of pre-trained CNNs, a CNN architecture especially for tomato leafs, which is using low system resources and giving high classification performance in shorter training times is presented. The use of fewer layers in the design of the model made the model applicable for use on mobile platforms. After the implementation of the model in mobile platforms, the proposed model can be used by tomato growers to detect disease more easily.