Abstract
Tomatoes, a popular crop, thrive on nearly any soil with good drainage. Identifying tomato diseases early on is crucial to maintaining quality and yield. This article proposes a hybrid model approach for early tomato disease detection from tomato leaf images. With the applied hybrid model approach, feature extraction was accomplished by employing a lightweight CNN model on images. These extracted features were then optimized by Whale optimization algorithm and utilized to train the SVM classifier for image classification. Validation of the test set utilizing Accuracy, Recall, Precision, and F1 score showed the proposed method achieved an accuracy score of 98.46%. Furthermore, the accuracy improvement obtained was 3.53% when using the proposed compared to the base CNN model. The findings validated that the proposed method outperforms state-of-the-art tomato disease detection methods with high accuracy and a lightweight model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agarwal, M., Singh, A., Arjaria, S., Sinha, A., Gupta, S.: Toled: tomato leaf disease detection using convolution neural network. Procedia Comput. Sci. 167, 293–301 (2020)
Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp. 1–6. IEEE (2017)
Bhujel, A., Kim, N.E., Arulmozhi, E., Basak, J.K., Kim, H.T.: A lightweight attention-based convolutional neural networks for tomato leaf disease classification. Agriculture 12(2), 228 (2022)
Debnath, A., et al.: A smartphone-based detection system for tomato leaf disease using efficientnetv2b2 and its explainability with artificial intelligence (ai). Sensors 23(21), 8685 (2023)
Elnaggar, S., Mohamed, A.M., Bakeer, A., Osman, T.A.: Current status of bacterial wilt (ralstonia solanacearum) disease in major tomato (solanum lycopersicum l.) growing areas in egypt. Arch. Agric. Environ. Sci 3(4), 399–406 (2018)
Hussein, M.A., Abbas, A.H.: Plant leaf disease detection using support vector machine. Al-Mustansiriyah J. Sci. 30(1), 105–110 (2019)
Ma, Y., Guo, G. (eds.): Support Vector Machines Applications. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02300-7
Maeda-Gutiérrez, V., et al.: Comparison of convolutional neural network architectures for classification of tomato plant diseases. Appl. Sci. 10(4), 1245 (2020)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
Mirjalili, S., Mirjalili, S.M., Saremi, S., Mirjalili, S.: Whale optimization algorithm: theory, literature review, and application in designing photonic crystal filters. In: Mirjalili, S., Song Dong, J., Lewis, A. (eds.) Nature-Inspired Optimizers. SCI, vol. 811, pp. 219–238. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-12127-3_13
Nawaz, M., et al.: A robust deep learning approach for tomato plant leaf disease localization and classification. Sci. Rep. 12(1), 18568 (2022)
Pantazi, X.E., Moshou, D., Tamouridou, A.A.: Automated leaf disease detection in different crop species through image features analysis and one class classifiers. Comput. Electron. Agric. 156, 96–104 (2019)
Rangarajan, A.K., Purushothaman, R., Ramesh, A.: Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Comput. Sci. 133, 1040–1047 (2018)
Richey, B., Majumder, S., Shirvaikar, M., Kehtarnavaz, N.: Real-time detection of maize crop disease via a deep learning-based smartphone app. In: Real-Time Image Processing and Deep Learning 2020, vol. 11401, pp. 23–29. SPIE (2020)
Sakkarvarthi, G., Sathianesan, G.W., Murugan, V.S., Reddy, A.J., Jayagopal, P., Elsisi, M.: Detection and classification of tomato crop disease using convolutional neural network. Electronics 11(21), 3618 (2022)
Trivedi, N.K., et al.: Early detection and classification of tomato leaf disease using high-performance deep neural network. Sensors 21(23), 7987 (2021)
Tulshan, A.S., Raul, N.: Plant leaf disease detection using machine learning. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–6. IEEE (2019)
Zamani, H., Nadimi-Shahraki, M.H.: Feature selection based on whale optimization algorithm for diseases diagnosis. Int. J. Comput. Sci. Inf. Secur. 14(9), 1243 (2016)
Zhang, S., Wang, H., Huang, W., You, Z.: Plant diseased leaf segmentation and recognition by fusion of superpixel, k-means and phog. Optik 157, 866–872 (2018)
Zhao, S., Peng, Y., Liu, J., Wu, S.: Tomato leaf disease diagnosis based on improved convolution neural network by attention module. Agriculture 11(7), 651 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hong, L.T.T., Huy, N.S., Tu, D.Q. (2024). Tomato Disease Detection from Tomato Leaf Images Using CNN-Based Feature Extraction, Feature Selection with Whale Optimization Algorithm, and SVM Classifier. In: Gervasi, O., Murgante, B., Garau, C., Taniar, D., C. Rocha, A.M.A., Faginas Lago, M.N. (eds) Computational Science and Its Applications – ICCSA 2024. ICCSA 2024. Lecture Notes in Computer Science, vol 14813. Springer, Cham. https://doi.org/10.1007/978-3-031-64605-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-64605-8_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-64604-1
Online ISBN: 978-3-031-64605-8
eBook Packages: Computer ScienceComputer Science (R0)