Detection and Classification of Some Diseases of Tomato Crops Using Transfer Learning

Document Type : Research Article-en

Author

• I. Ahmadi

Department of Genetics and Plant Production Engineering, Institute of Agriculture, Water, Food and Nutraceuticals, Isf. C., Islamic Azad University, Isfahan, Iran

https://doi.org/10.22067/jam.2024.88500.1258

Abstract

In the context of plant diseases, the selection of appropriate preventive measures, such as correct pesticide application, is only possible when plant diseases have been diagnosed quickly and accurately. In this study, a transfer learning model based on the pre-trained EfficientNet model was implemented to detect and classify some diseases in tomato crops, using an augmented training dataset of 2340 images of tomato plants. The study's findings indicate that during the model's validation phase, the rate of image categorization was roughly 5 fps (frames per second), which makes sense for a deep learning model operating on a laptop computer equipped with a standard CPU. Furthermore, the model was learned well because increasing the number of epochs no longer improved its accuracy. After all, the curves of the train and test accuracies, as well as the losses versus epoch numbers, remained largely horizontal for epoch numbers greater than 20. Notably, the highest coefficient of variation across these four cases was only 7%. Furthermore, the cells of the primary diagonal of the confusion matrix were filled with larger numbers in comparison with the values of the other cells; precisely, 88.8%, 7.7%, and 3.3% of the remaining cells of the matrix (cells of the primary diagonal excluded) were filled with 0, 1, and 2, respectively. The model's performance metrics are: sensitivity 85%, specificity 98%, precision 86%, F1-score 84%, and accuracy 85%.

Keywords

• Confusion Matrix
• EfficientNet Model
• Model Accuracy

Main Subjects

• Image Processing

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

References

1. Agarwal, M., Singh, A., Arjaria, S., Sinha, A., & Gupta S. T. (2020). Tomato leaf disease detection using convolution neural network. Procedia Computer Science, 167, 293-301. https://doi.org/10.1016/j.procs.2020.03.225
2. Ahmed, I., & Yadav, P. K. (2023). A systematic analysis of machine learning and deep learning based approaches for identifying and diagnosing plant diseases. Sustainable Operations and Computers, 4, 96-104. https://doi.org/10.1016/j.susoc.2023.03.001
3. Ali, M. M., Bachik, N. A., Muhadi, N. A., Tuan Yusof, T. N., & Gomes, C. (2019). Nondestructive techniques of detecting plant diseases: A review. Physiological and Molecular Plant Pathology, 108, 101426. https://doi.org/10.1016/j.pmpp.2019.101426
4. Anjna, Sood, M., & Singh, P. K. (2020). Hybrid system for detection and classification of plant disease using qualitative texture features analysis. Procedia Computer Science, 167, 1056-1065. https://doi.org/10.1016/j.procs.2020.03.404
5. Balaji, V., Anushkannan, N. K., Narahari, Sujatha Canavoy, Rattan, Punam, Verma, Devvret, Awasthi, Deepak Kumar, Pandian, A. Anbarasa, Veeramanickam, M. R. M., Mulat, & Molla Bayih (2023). Deep transfer learning technique for multimodal disease classification in plant images. Contrast Media & Molecular Imaging, 5644727. https://doi.org/10.1155/2023/5644727
6. Batool, A., Hyder, S. B., Rahim, A., Waheed, N., & Asghar, M.A. (2020). Classification and identification of tomato leaf disease using deep neural network. International Conference on Engineering and Emerging Technologies (ICEET). https://doi.org/10.1109/ICEET48479.2020.9048207
7. Burhan, S. A., Minhas, D. S., Tariq, D. A., & Nabeel, H. M. (2020). Comparative study of deep learning algorithms for disease and pest detection in rice crops. 12^th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). https://doi.org/10.1109/ECAI50035.2020.9223239
8. Genaev, M. A., Skolotneva, E. S., Gultyaeva, E. I., Orlova, E. A., Bechtold, N. P., & Afonnikov, D. A. (2021). Image-based wheat fungi diseases identification by deep learning. Plants, 10(8), 1-21. https://doi.org/10.3390/plants10081500
9. Guerrero-Ibanez, A., & Reyes-Munoz, A. (2023). Monitoring tomato leaf disease through convolutional neural networks. Electron, 12(1), 1-15. https://doi.org/10.3390/electronics12010229
10. Bharali, P., Bhuyan, C., & Boruah, A. (2019). Plant Disease Detection by Leaf Image Classification Using Convolutional Neural Network. In: Gani, A., Das, P., Kharb, L., Chahal, D. (eds) Information, Communication and Computing Technology. ICICCT 2019. Communications in Computer and Information Science, vol 1025. Springer, Singapore. https://doi.org/10.1007/978-981-15-1384-8_16
11. Huang, Z., Su, L., Wu, J., & Chen, Y. (2023). Rock Image Classification Based on EfficientNet and Triplet Attention Mechanism. Applied Science Letters, 13, https://doi.org/10.3390/app13053180
12. Jasim, M. A., & Al-Tuwaijari, J. M. (2020). Plant leaf diseases detection and classification using image processing and deep learning techniques. International Conference on Computer Science and Software Engineering. https://doi.org/10.1109/CSASE48920.2020.9142097
13. Jena, L., Sethy, P. K., & Behera, S. K. (2021). Identification of wheat grain using geometrical feature and machine learning. In: 2nd international conference for emerging technology (INCET) (pp. 1_6).
14. Khan, R. U., Khan, K., Albattah, W., & Qamar, A.M. (2021). Image-based detection of plant diseases: from classical machine learning to deep learning journey. Wireless Communications and Mobile Computing, 1-13. https://doi.org/10.1155/2021/5541859
15. Kirola, M., Joshi, K., Chaudhary, S., Singh, N., Anandaram, H., & Gupta, A. (2022). Plants diseases prediction framework: a imagebased system using deep learning. Proc IEEE World Conf Appl Intell Comput. https://doi.org/10.1109/AIC55036.2022.9848899
16. Liu, J., & Wang, X. (2021). Plant diseases and pests detection based on deep learning: a review. Plant Methods, 17(1), 1-18. https://doi.org/10.1186/s13007-021-00722-9
17. Shah, J. P., Harshadkumar, B., Prajapati, V. K., & Dabhi. (2016). A survey on detection and classification of rice plant diseases. In: IEEE international conference on current trends in advanced computing (ICCTAC).
18. Upadhyay, S. K., & Kumar, A. (2022). A novel approach for rice plant diseases classification with deep convolutional neural network. International Journal of Information Technology, 14(1):185-99. https://doi.org/10.1007/s41870-021-00817-5
19. Zhao, S., Peng, Y., Liu, J., & Wu, S. (2021). Tomato leaf disease diagnosis based on improved convolution neural network by attention module. Agriculture, https://doi.org/10.3390/agriculture11070651