Abstract
Agriculture is efficient from an economic and industrial point of view. The majority of countries are trying to be self-sufficient to be able to feed their people. But unfortunately, several states are suffering enormously and are unable to join the standing up to satisfy their populations in sufficient quantities. Despite technological advances in scientific research and advances in genetics to improve the quality and quantity of agricultural products, today we find people who die of death. In addition to famines caused by wars and ethnic conflicts and above all plant diseases that can devastate entire crops and have harmful consequences for agricultural production. With the advancement of artificial intelligence and vision from computers, solutions have brought to many problems. Smartphone applications based on deep learning using convolutionary neural network for deep learning can detect and classify plant diseases according to their types. Thanks to these processes, many farmers have solved their harvesting problems (plant diseases) and considerably improved their yield and the quality of the harvest. In our article, we propose to study the plant disease (tomato) using the PlantVillage [1] database with 18,162 images for 9 diseased classes and one seine class. The use of CNN architectures DenseNet169 [2] and InceptionV3 [3] made it possible to detect and classify the various diseases of the tomato plant. We used transfer learning technology with a batch-size of 32 as well as the RMSprop and Adam optimizers. We, therefore, opted for a range of 80% for learning and 20% for the test with a period number of 100. We evaluated our results based on five criteria (number of parameters, top accuracy, accuracy, top loss, score) with an accuracy of 100%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hughes, D., Salathé, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing. arXiv preprint arXiv:1511.08060 (2015): n. pag
Huang, G., Liu, Z., K. Q. Weinberger, and L. van der Maaten, “Densely connected convolutional networks,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2017, pp. 4700–4708
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 26 June–1 July 2016, pp. 770–778 (2016)
Bachman, S.: State of the World’s Plants Report. Royal Botanic Gardens, Kew, p. 7/84 (2016) (ISBN 978-1-84246-628-5)
Hanssen, I.M., Lapidot, M.: Major tomato viruses in the Mediterranean basin. In: Loebenstein, G., Lecoq, H. (eds.) Advances in Virus Research, vol. 84, pp. 31–66. Academic Press, San Diego (2012)
Market developments in Fruit and Vegetables Algeria [https://meys.eu/media/1327/market-developments-in-fruit-and-vegetables-algeria.pdf], MEYS Emerging Markets Research
Akhtar, A., Khanum, A., Khan, S.A., Shaukat, A.: Automated plant disease analysis (APDA): performance comparison of machine learning techniques. In: Proceedings of the 11th International Conference on Frontiers of Information Technology, pp. 60–65 (2013)
Kawasaki, Y., Uga, H., Kagiwada, S., Iyatomi, H.: Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. In: Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14–16, 2015. Proceedings, Part II, 638–645 (2015)
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell, Neurosci (2016)
Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)
Nachtigall, L.G., Araujo, R.M., Nachtigall, G.R.: Classification of apple tree disorders using convolutional neural networks. In: Proceedings of the 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 472–476. San Jose, CA 6–8 November 2016
Lu, Y., Yi, S., Zeng, N., Liu, Y., Zhang, Y.: Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267, 378–384 (2017)
Wang, G., Sun, Y., Wang, J.: Automatic image-based plant disease severity estimation using deep learning. Comput. Intell. Neurosci. 2917536 (2017)
Rangarajan, A.K., Purushothaman, R., Ramesh, A.: Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Comput. Sci. 133, 1040–1047 (2018)
Khandelwal, I., Raman, S.: Analysis of transfer and residual learning for detecting plant diseases using images of leaves. Computational Intelligence: Theories. Applications and Future Directions-Volume II, pp. 295–306. Springer, Singapore (2019)
Maeda-Gutiérrez, V., Galván-Tejada, C.E., Zanella-Calzada, L.A., Celaya-Padilla, J.M., Galván-Tejada, J.I., Gamboa-Rosales, H., Luna-García, H., Magallanes-Quintanar, R., Guerrero Méndez, C.A., Olvera-Olvera, C.A.: Comparison of convolutional neural network architectures for classification of tomato plant diseases. Appl. Sci. 10, 1245 (2020)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, vol. abs/1409.1556 (2014)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. CACM (2017)
Acknowledgements
I sincerely thank Doctor Mechab Boubaker from the University of Djillali Liabes of Sidi Bel Abbes for encouraging and supporting me throughout this work and also for supporting me in hard times because it is thanks to him that I was able to do this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hammou, D.R., Boubaker, M. (2022). Tomato Plant Disease Detection and Classification Using Convolutional Neural Network Architectures Technologies. In: Ben Ahmed, M., Teodorescu, HN.L., Mazri, T., Subashini, P., Boudhir, A.A. (eds) Networking, Intelligent Systems and Security. Smart Innovation, Systems and Technologies, vol 237. Springer, Singapore. https://doi.org/10.1007/978-981-16-3637-0_3
Download citation
DOI: https://doi.org/10.1007/978-981-16-3637-0_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3636-3
Online ISBN: 978-981-16-3637-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)