Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models

Year 2023, , 214 - 232, 15.12.2023

Yunus Camgözlü , Yakup Kutlu

https://doi.org/10.28978/nesciences.1405175

Cited By: 2

Abstract

It is important to identify a high-performance model that can classify all leaves and even differentiate according to regional variations of the same leaf type. In this study, a leaf classification model was created using 5 different datasets with different number of images and compared with models. For this purpose, 4 different pre-trained models called VGG16, InceptionV3, MobileNet and DenseNet are used. In addition, a new model was proposed and model training was carried out using these datasets . Using the all models, inputs are transformed into feature vectors by parameter transfer method and used for classification with the nearest neighbor algorithm and support vector machine. The performance of the classifications were compared with similar studies in the literature.

Keywords

Artificial intelligence, machine learning, deep learning, parameter transfer, pre-trained model, feature transformation

References

• Anubha Pearline, S., Sathiesh Kumar, V., Harini, S. (2019). A study on plant recognition using conventional image processing and deep learning approaches. Journal of Intelligent & Fuzzy Systems, 36(3):1997-2004. https://doi.org/10.3233/JIFS-169911.
• Atabay, H. A. (2016). A convolutional neural network with a new architecture applied on leaf classification. IIOAB J, 7(5):226-331.
• Barre, P., Stöver, B. C., Müller, K. F., Steinhage, V. (2017). Leafnet: A Computer vision system for automatic plant species identification. Ecological Informatics, 40:50-56. https://doi.org/10.1016/j.ecoinf.2017.05.005.
• Beikmohammadi, A. & Faez, K. (2018). Leaf classification for plant recognition with deep transfer learning. In 2018 4th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), pages 21-26. IEEE.
• Camgözlü, Y., & Kutlu, Y. (2019). Analysis of pooling effect on CNN using leaf database. Natural and Engineering Sciences, 4(3):115-121.
• Camgozlu, Y., & Kutlu, Y. (2020). Examining the difference between image size, background color, gray picture and color picture in leave classification with deep learning. International Journal of Intelligent Systems and Applications, 3, 130-133.
• Camgözlü, Y., & Kutlu, Y. (2021). Yaprak Sınıflandırmak için Yeni Bir Evrişimli Sinir Ağı Modeli Geliştirilmesi . Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi , 8 (2) , 567-574 . https://doi.org/10.35193/bseufbd.887643.
• Chouhan, S. S., Singh, U. P., Kaul, A., Jain, S. (2019). A data repository of leaf images: Practice towards plant conservation with plant pathology. In 2019 4th International Conference on Information Systems and Computer Networks (ISCON), pages 700-707. IEEE.
• Gajjar, V. K., Nambisan, A. K., & Kosbar, K. L. (2022). Plant Identification in a Combined-Imbalanced Leaf Dataset. IEEE Access, 10, 37882-37891. https://doi.org/1010.1109/ACCESS.2022.3165583.
• Ganguly, S., Bhowal, P., Oliva, D., & Sarkar, R. (2022). BLeafNet: A Bonferroni mean operator based fusion of CNN models for plant identification using leaf image classification. Ecological Informatics, 69, 101585. https://doi.org/10.1016/j.ecoinf.2022.101585.
• Hewitt, C. & Mahmoud, M. (2018). Shape-only features for plant leaf identification. arXiv preprint arXiv:1811.08398. https://doi.org/10.48550/arXiv.1811.08398.
• Howard, A., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andretto, M. Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv: 1704.04861. https://doi.org/10.48550/arXiv.1704.04861.
• Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4700-4708.
• Ilievski, A., Zdraveski, V., Gusev, M. (2018). How CUDA powers the machine learning revolution. In 2018 26th Telecommunications Forum (TELFOR2018). 420-425.
• Jiang, W., Özaktas, B. B., Mantri, N., Tao, Z., Lu, H. (2013). Classification of camellia species from 3 sections using leaf anatomical data with back-propagation neural networks and support vector machines. Turkish Journal of Botany, 37(6):1093-1103. https://doi.org/10.3906/bot-1210-21.
• Kadir, A., Nugroho, L. E., Susanto, A., Santosa, P. I. (2013). Leaf classification using shape, color, and texture features. arXiv preprint arXiv:1401.4447. https://doi.org/10.48550/arXiv.1401.4447.
• Kulkarni, A., Rai, H., Jahagirdar, K., Upparamani, P. (2013). A leaf recognition technique for plant classification using RBPNN and zernike moments. International Journal of Advanced Research in Computer and Communication Engineering, 2(1):984-988.
• Kumar, N., Belhumeur, P. N., Biswas, A., Jacobs, D. W., Kress, W. J., Lopez, I. C., Soares, J. V. (2012). Leafsnap: A computer vision system for automatic plant species identification. In European conference on computer vision, 502-516.
• Kutlu, Y., Altan, G., İşçimen, B., Doğdu, S. A., Turan, C. (2017). Recognition of species of triglidae family using deep learning. Journal of the Black Sea/Mediterranean Environment, 23(1), 56-65.
• Kwolek, B. (2005). Face detection using convolutional neural networks and gabor filters. In International Conference on Artificial Neural Networks, 551-556..
• Lavania, S. & Matey, P. S. (2014). Leaf recognition using contour based edge detection and sift algorithm. In 2014 IEEE International Conference on Computational Intelligence and Computing Research, pages 1-4.
• Lee, S. H., Chan, C. S., Mayo, S. J., Remagnino, P. (2017). How deep learning extracts and learns leaf features for plant classification. Pattern Recognition, 71:1-13. https://doi.org/10.1016/j.patcog.2017.05.015.
• Mostafa, S. I., Abd El-Latif, Y. M., Reda, N. M. (2020). Fast And Accurate System For Leaf Recognition. International Journal of Computer Sciences and Engineering, 8(8), 73-79. https://doi.org/10.26438/ijcse/v8i8.7379.
• Padao, F. R. F. & Maravillas, E. A. (2015). Using naive bayesian method for plant leaf classification based on shape and texture features. In 2015 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 1-5.
• Petkov, N. (2018). Automatic segmentation of indoor and outdoor scenes from visual lifelogging. In Applications of Intelligent Systems: Proceedings of the 1st International APPIS Conference, 310, 194.
• Raj, A. P. S. S. & Vajravelu, S. K. (2019). Ddla: dual deep learning architecture for classification of plant species. IET Image Processing, 13(12):2176-2182. https://doi.org/10.1049/iet-ipr.2019.0346.
• Shah, M. P., Singha, S., & Awate, S. P. (2017). Leaf classification using marginalized shape context and shape+ texture dual-path deep convolutional neural network. In 2017 IEEE International Conference on Image Processing (ICIP), 860-864.
• Silva, P. F., Marcal, A. R., Silva, R. M. (2013). Evaluation of features for leaf discrimination. In International Conference Image Analysis and Recognition, 197-204.
• Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv,1409.1556. https://doi.org/10.48550/arXiv.1409.1556.
• Soderkvist, O. (2001). Computer vision classification of leaves from swedish trees (MsC thesis), Linkoping University, Linkoping, Sweden.
• Soucy, P. & Mineau, G. W. (2001). A simple knn algorithm for text categorization. In Proceedings 2001 IEEE International Conference on Data Mining, 647-648. IEEE.
• Sujith, A. & Neethu, R. (2021). Classification of plant leaf using shape and texture features. In Inventive Communication and Computational Technologies, 269-282.
• Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2818-2826.
• Tomar, D. & Agarwal, S. (2016). Leaf recognition for plant classification using direct acyclic graph based multi- class least squares twin support vector machine. International Journal of Image and Graphics, 16(03),1650012. https://doi.org/10.1142/S0219467816500121.
• Tsolakidis, D. G., Kosmopoulos, D. I., Papadourakis, G. (2014). Plant leaf recognition using zernike moments and histogram of oriented gradients. In Hellenic Conference on Artificial Intelligence, 406-417.
• Wang, X., Du, W., Guo, F., Hu, S. (2020). Leaf recognition based on elliptical half gabor and maximum gap local line direction pattern. IEEE Access, 8:39175-39183. https://doi.org/10.1109/ACCESS.2020.2976117.
• Wang, Z., Sun, X., Ma, Y., Zhang, H., Ma, Y., Xie, W., Zhang, Y. (2014). Plant recognition based on intersecting cortical model. In 2014 International joint conference on neural networks (IJCNN), 975-980.
• Wang, Z., Sun, X., Yang, Z., Zhang, Y., Zhu, Y., Ma, Y. (2018). Leaf recognition based on DPCNN and BOW. Neural Processing Letters, 47(1):99-115. https://doi.org/10.1007/s11063-017-9635-1.
• Wu, S. G., Bao, F. S., Xu, E. Y., Wang, Y.-X., Chang, Y.-F., Xiang, Q.-L. (2007). A leaf recognition algorithm for plant classification using probabilistic neural network. In 2007 IEEE International Symposium on Signal Processing and Information Technology, 11-16. IEEE.
• Zhang, Y., Cui, J., Wang, Z., Kang, J., Min, Y. (2020). Leaf image recognition based on bag of features. Applied Sciences, 10(15):5177. https://doi.org/10.3390/app10155177.