A Novel Approach for Tomato Leaf Disease Classification with Deep Convolutional Neural Networks

Abstract

Computer-aided automation systems for the detection of plant diseases represent a challenging and highly impactful research domain in the field of agriculture. Tomatoes, a major and globally significant agricultural commodity, are cultivated in large quantities. This study introduces a novel approach for the automated detection of diseases on tomato leaves, leveraging both classical machine learning methods and deep neural networks for image classification. Specifically, classical learning methods employed the local binary pattern (LBP) technique for feature extraction, while classification tasks were carried out using extreme learning machines, k-nearest neighborhood (kNN), and support vector machines (SVM). In contrast, a novel convolutional neural network (CNN) framework, complete with unique parameters and layers, was utilized for deep learning. The results of this study demonstrate that the proposed approach outperforms state-of-the-art studies in terms of accuracy. The classification process covered various scenarios, including binary classification (healthy vs. unhealthy), 6-class classification, and 10-class classification for distinguishing different types of diseases. The findings indicate that the CNN model consistently outperformed classical learning methods, achieving accuracy rates of 99.5%, 98.50%, and 97.0% for 2-class, 6-class, and 10-class classifications, respectively. Future research may explore the use of computer-aided automated systems to detect diseases in diverse plant species.

Keywords

• Automated agriculture
• Machine learning in agriculture
• Convolutional neural networks in plant pathology
• Deep learning in agriculture
• Smart farming

References

1. Adebayo S E, Hashim N, Abdan K & Hanafi M (2016). Application and potential of backscattering imaging techniques in agricultural and food processing–A review. Journal of Food Engineering, 169: 155–164
2. Altuntaş Y & Kocamaz F (2021). Deep feature extraction for detection of tomato plant diseases and pests based on leaf images. Celal Bayar University Journal of Science, 17(2): 145–157
3. Anonymous (2021a). Complex Projective 4-Space. https://cp4space.wordpress.com/page/3/ [2021-06-10] Anonymous (2021b). Tomato Plant Disease Detection by RAVI . https://www.kaggle.com/ravibalas1999/tomotoplant-dosease-detection [2021-07-02]
4. Anonymous (2021c). VGG16 by MMDRAGE . https://www.kaggle.com/mmdrage/vgg16-fine-tunning-and-98-55-val-acc [2021-07-02]
5. Arakeri M P (2016). Computer vision based fruit grading system for quality evaluation of tomato in agriculture industry. Procedia Computer Science, 79: 426–433
6. Arya S, Mount D M, Netanyahu N S, Silverman R & Wu A Y (1998). An optimal algorithm for approximate nearest neighbor searching fixed dimensions. Journal of the ACM (JACM), 45 (6): 891–923
7. Bhandari M, Shahi T B, Neupane A & Walsh K B (2023). BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model. Journal of Imaging, 9 (2): 53. https://doi.org/10.3390/jimaging9020053
8. Brahimi M, Boukhalfa K & Moussaoui A (2017). Deep learning for tomato diseases: classification and symptoms visualization. Applied Artificial Intelligence, 31 (4): 299–315

9. Burgos-Artizzu X P, Ribeiro A, Tellaeche A, Pajares G & Fernández-Quintanilla C (2010). Analysis of natural images processing for the extraction of agricultural elements. Image and Vision Computing, 28 (1): 138–149
10. Chen X, Zhou G, Chen A, Yi J, Zhang W & Hu Y (2020). Identification of tomato leaf diseases based on combination of ABCK-BWTR and B-ARNet. Computers and Electronics in Agriculture, 178: 105730
11. Durmuş H, Güneş E O & Kırcı M (2017). Disease detection on the leaves of the tomato plants by using deep learning. In: Proceedings of the 6th International Conference on Agro-Geoinformatics (IEEE), 07-10 August, Fairfax VA, USA, pp. 1–5
12. Dutta M K, Sengar N, Minhas N, Sarkar B, Goon A & Banerjee K (2016). Image processing based classification of grapes after pesticide exposure. LWT-Food Science and Technology, 72: 368–376
13. Gerdan D, Koç C & Vatandaş M (2023). Diagnosis of Tomato Plant Diseases Using Pre-Trained Architectures and A Proposed Convolutional Neural Network Model. Journal of Agricultural Sciences (Tarım Bilimleri Dergisi), 29(2): 618-629 https://doi.org/10.15832/ankutbd.957265
14. Gonzalez-Huitron V, León-Borges J A, Rodriguez-Mata A E, Amabilis-Sosa L E, Ramírez-Pereda B & Rodriguez H (2021). Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4. Computers and Electronics in Agriculture, 181: 105951
15. Huang G B, Zhou H, Ding X & Zhang R (2011). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42 (2): 513–529
16. Huang G B, Zhu Q Y & Siew C K (2004). Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of the IEEE International Joint Conference on Neural Networks, 25-29 July, Budapest, Hungary, pp. 985–990
17. Huang G B, Zhu Q Y & Siew C K (2006). Extreme learning machine: theory and applications. Neurocomputing, 70 (1–3): 489–501
18. Jiang F, Lu Y, Chen Y, Cai D & Li G (2020). Image recognition of four rice leaf diseases based on deep learning and support vector machine. Computers and Electronics in Agriculture, 179: 105824
19. Kapucuoglu K (2011). Plant Diseases Classification Using AlexNet. https://www.kaggle.com/koksal1994/plant-diseases-classification-using-alexnet [2021-08-03]
20. Karthik R, Hariharan M, Anand S, Mathikshara P, Johnson A & Menaka R (2020). Attention embedded residual CNN for disease detection in tomato leaves. Applied Soft Computing, 86: 105933
21. Kuta Ł, Li Z, Stopa R, Komarnicki P & Słupska M (2020). The influence of manual harvesting on the quality of picked apples and the Picker’s muscle load. Computers and Electronics in Agriculture, 175: 105511
22. Lamrahi N (2021). Tomato Dataset. https://www.kaggle.com/noulam/tomato [2021-04-01]
23. Lim J S & Oppenheim A V (1979). Enhancement and bandwidth compression of noisy speech. Proceedings of the IEEE, 67 (12): 1586–1604
24. Liming X & Yanchao Z (2010). Automated strawberry grading system based on image processing. Computers and electronics in agriculture, 71: S32–S39
25. Methun N R, Yasmin R, Begum N, Rajbongshi A & Islam M E (2021). Carrot disease recognition using deep learning approach for sustainable agriculture. International Journal of Advanced Computer Science and Applications, 12 (9): 732-741
26. Ojala T, Pietikäinen M & Mäenpää T (2000). Gray scale and rotation invariant texture classification with local binary patterns, Computer Vision - ECCV 2000 (Springer), 1842: 404–420
27. Ouhami M, Es-Saady Y, Hajji M El, Hafiane A, Canals R & Yassa M El (2020). Deep transfer learning models for tomato disease detection, In: Proceedings of the Image and Signal Processing: 9th International Conference, ICISP 2020, Marrakesh, Morocco, 2020, Proceedings 9 (pp. 65-73). Springer International Publishing.2020. 65–73. Springer
28. Park K, ki Hong Y, hwan Kim G, & Lee J (2018). Classification of apple leaf conditions in hyper-spectral images for diagnosis of Marssonina blotch using mRMR and deep neural network. Comput. Electron. Agric, 148: 179–187
29. Rahman S U, Alam F, Ahmad N & Arshad S (2023). Image processing based system for the detection, identification and treatment of tomato leaf diseases. Multimedia Tools and Applications, 82 (6): 9431–9445. https://doi.org/10.1007/s11042-022-13715-0
30. Rehman Z U, Khan M A, Ahmed F, Damaševičius R, Naqvi S R, Nisar W & Javed K (2021). Recognizing apple leaf diseases using a novel parallel real‐time processing framework based on MASK RCNN and transfer learning: An application for smart agriculture. IET Image Processing, 15 (10): 2157–2168
31. Schölkopf B & Smola A J (2002). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.
32. Sembiring A, Away Y, Arnia F & Muharar R (2021). Development of concise convolutional neural network for tomato plant disease classification based on leaf images. Journal of Physics: Conference Series IOP Publishing. 1845(1): 012009
33. Sethy P K, Barpanda N K, Rath A K & Behera S K (2020). Deep feature based rice leaf disease identification using support vector machine. Computers and Electronics in Agriculture, 175: 105527
34. Temniranrat P, Kiratiratanapruk K, Kitvimonrat A, Sinthupinyo W & Patarapuwadol S (2021). A system for automatic rice disease detection from rice paddy images serviced via a Chatbot. Computers and Electronics in Agriculture, 185: 106156
35. Tian K, Li J, Zeng J, Evans A & Zhang L (2019). Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm. Computers and Electronics in Agriculture, 165: 104962
36. Tian K, Zeng J, Song T, Li Z, Evans A & Li J (2022). Tomato leaf diseases recognition based on deep convolutional neural networks. Journal of Agricultural Engineering, 54(1). https://doi.org/10.4081/jae.2022.1432
37. Tm P, Pranathi A, SaiAshritha K, Chittaragi N B & Koolagudi S G (2018). Tomato leaf disease detection using convolutional neural networks, In: Proceedings of the Eleventh International Conference on Contemporary Computing (IC3) pp. 1–5
38. Wspanialy P & Moussa M (2020). A detection and severity estimation system for generic diseases of tomato greenhouse plants. Computers and Electronics in Agriculture, 178: 105701
39. Xu P, Wu G, Guo Y, Yang H & Zhang R (2017). Automatic wheat leaf rust detection and grading diagnosis via embedded image processing system. Procedia Computer Science, 107: 836–841
40. Zhong Y & Zhao M (2020). Research on deep learning in apple leaf disease recognition. Computers and Electronics in Agriculture, 168: 105146