PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Tomato is the most popular and cultivated crop in the world. Nevertheless, the quality and quantity of tomato crops have been declining due to various diseases that afflict tomato crops. Hence, it becomes necessary to detect the disease early to prevent crop damage and increase the yield. The proposed model in this article predicts the infected tomato leaf images (9 classified diseases and also healthy class) obtained from the Plant Village dataset. In this model, Transfer learning was used to extract features from images by VGG16, yielding a high dimension of 25088 features. Overfitting is a commonly anticipated problem because of the higher dimensionality of data. To mitigate this problem, the authors have adopted a novel dimensional reduction-based technique: filter methods, feature extraction techniques like Principal Components Analysis (PCA), and the Boruta feature selection technique of wrapper methods. This adoption enables the proposed model to attain a significantly improved high accuracy of 95.68% and 95.79% in MLP and VGG16, respectively, by reducing its initial dimension on the tomato dataset containing 18160 images across 10 classes.

• Ali, J., Khan, R., Ahmad, N., & Maqsood, I. (2012). Random forests and decision trees. International Journal of Computer Science, 9(5), Article 272.

• Awad, M., & Khanna, R. (2015). Support vector machines for classification. In Efficient Learning Machines (pp. 39-66). Apress Berkeley, CA. https://doi.org/10.1007/978-1-4302-5990-9_3

• Behera, B., Kumaravelan, G., & Kumar, P. (2019). Performance evaluation of deep learning algorithms in biomedical document classification. In 2019 11th International Conference on Advanced Computing (ICoAC) (pp. 220-224). IEEE Publishing. https://doi.org/10.1109/ICoAC48765.2019.246843

• Cerda, P., & Varoquaux, G. (2020). Encoding high-cardinality string categorical variables. IEEE Transactions on Knowledge and Data Engineering, 34(3), 1164-1176. https://doi.org/10.1109/TKDE.2020.2992529

• Chen, G., & Chen, J. (2015). A novel wrapper method for feature selection and its applications. Neurocomputing, 159, 219-226. https://dl.acm.org/doi/abs/10.5555/2781902.2782171

• Chuanlei, Z., Shanwen, Z., Jucheng, Y., Yancui, S., & Jia, C. (2017). Apple leaf disease identification using genetic algorithm and correlation-based feature selection method. International Journal of Agricultural and Biological Engineering, 10(2), 74-83. https://doi.org/10.3965/j.ijabe.20171002.2166

• Doquire, G., & Verleysen, M. (2013). A graph Laplacian based approach to semi-supervised feature selection for regression problems. Neurocomputing, 121, 5-13. https://doi/abs/10.1016/j.neucom.2012.10.028

• Dreiseitl, S., & Ohno-Machado, L. (2002). Logistic regression and artificial neural network classification models: A methodology review. Journal of Biomedical Informatics, 35(5-6), 352-359. https://doi.org/10.1016/S1532-0464(03)00034-0

• Durmuş, H., Güneş, E. O., & Kırcı, M. (2017). Disease detection on the leaves of the tomato plants by using deep learning. In 2017 6th International Conference on Agro-Geoinformatics (pp. 1-5). IEEE Publishing. https://10.1109/Agro-Geoinformatics.2017.8047016

• Gadekallu, T. R., Rajput, D. S., Reddy, M., Lakshmanna, K., Bhattacharya, S., Singh, S., & Alazab, M. (2021). A novel PCA-whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. Journal of Real-Time Image Processing, 18(4), 1383-1396. https://doi.org/10.1007/s11554-020-00987-8

• Gao, B., & Pavel, L. (2017). On the properties of the softmax function with application in game theory and reinforcement learning. arXiv Preprint. https://doi.org/10.48550/arXiv.1704.00805

• Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2003). KNN model-based approach in classification. In R. Meersman, Z. Tari & D. C. Schmidt (Eds.), OTM Confederated International Conferences” On the Move to Meaningful Internet Systems” (pp. 986-996). Springer. https://doi.org/10.1007/978-3-540-39964-3_62

• Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016). Squeeze Net: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv Preprint. https://doi.org/10.48550/arXiv.1602.07360

• Khammari, A., Nashashibi, F., Abramson, Y., & Laurgeau, C. (2005). Vehicle detection combining gradient analysis and AdaBoost classification. In Proceedings. 2005 IEEE Intelligent Transportation Systems (pp. 66-71). IEEE Publishing. https:// doi:10.1109/ITSC.2005.1520202

• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25 (p. 1). Morgan Kaufmann Publishers.

• Kursa, M. B., & Rudnicki, W. R. (2010). Feature selection with the Boruta package. Journal of Statistical Software, 36, 1-13. https://doi.org/10.18637/jss.v036.i11

• Li, J., Si, Y., Xu, T., & Jiang, S. (2018). Deep convolutional neural network-based ECG classification system using information fusion and one-hot encoding techniques. Mathematical Problems in Engineering, 2018, Article 7354081. https://doi.org/10.1155/2018/7354081

• Ma, H., Li, Y., Chen, Q., Zhang, L., & Xu, J. (2018). A single-stage integrated boost-LLC AC–DC converter with quasi-constant bus voltage for multichannel LED street-lighting applications. IEEE Journal of Emerging and Selected Topics in Power Electronics, 6(3), 1143-1153. https://doi.org/10.1109/JESTPE.2018.2847327

• Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, Article 1419. https://doi.org/10.3389/fpls.2016.01419

• Mudrova, M., & Procházka, A. (2005, November 15). Principal component analysis in image processing. In Proceedings of the MATLAB Technical Computing Conference (pp. 1-4). Prague, Czech Republic.

• Noriega, L. (2005). Multilayer perceptron tutorial. Staffordshire University. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=4c8339b893423f1e14e34cc1543faee4e5ee4244

• Ramchoun, H., Ghanou, Y., Ettaouil, M., & Idrissi, M. A. J. (2016). Multilayer perceptron: Architecture optimization and training. International Journal of Interactive Multimedia and Artificial Intelligence, 4(1), 26-30. http://doi.org/10.9781/ijimai.2016.415

• Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv Preprint. https://doi.org/10.48550/arXiv.1409.1556

• Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427-437. https://doi.org/10.1016/j.ipm.2009.03.002

• Tammina, S. (2019). Transfer learning using VGG-16 with deep convolutional neural network for classifying images. International Journal of Scientific and Research Publications, 9(10), 143-150. https://doi.org/10.29322/IJSRP.9.10.2019.p9420

• Tang, Y., & Wu, X. (2016). Saliency detection via combining region-level and pixel-level predictions with CNNs. In European Conference on Computer Vision (pp. 809-825). Springer. https://doi.org/10.48550/arXiv.1608.05186

• Tm, P., Pranathi, A., SaiAshritha, K., Chittaragi, N. B., & Koolagudi, S. G. (2018). Tomato leaf disease detection using convolutional neural networks. In 2018 eleventh international conference on contemporary computing (IC3) (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/IC3.2018.8530532

• Torlay, L., Perrone-Bertolotti, M., Thomas, E., & Baciu, M. (2017). Machine learning–XGBoost analysis of language networks to classify patients with epilepsy. Brain Informatics, 4(3), 159-169. https://doi.org/10.1007/s40708-017-0065-7

• Zhang, M. L., & Zhou, Z. H. (2005). A k-nearest neighbor-based algorithm for multi-label classification. In 2005 IEEE International Conference on Granular Computing (Vol. 2, pp. 718-721). IEEE Publishing. https://doi.org/10.1109/GRC.2005.1547385