Determining Categories of Leaf Images Using Transfer Learning of VGGNet
Abstract views: 5
/ 
PDF downloads: 10
Keywords:
Leaf Image Categorization, VGGNet16, Convolutional Neural Networks, Transfer Learning, Augmented ImagesAbstract
Recognizing plant species from their leaves holds great value across many domains, from
agriculture and forestry to pharmacology and the assessment of regional biodiversity. This research
focused on creating automated systems capable of identifying 12 distinct plant species solely from leaf
images. To accomplish this, we utilized the powerful VGGNet16 convolutional neural network. After
dividing our leaf images into a train set and a test set, we enriched the training data through an on-the-fly
augmentation process. By applying transformations like scaling, translation, and rotation to the images
during the network's training epochs, we improved its ability to generalize to future data, thereby
enhancing its overall performance. We applied with two approaches as training a standard VGGNet16
network and training a pre-trained VGGNet16 that had already learned features from a massive image
database. The effectiveness of these systems, configured with different parameters, was benchmarked on
the test dataset using classification metrics as F1-Score , Accuracy, and the ROC curve. Performance of
the pre-trained VGGNet16 network was observed to be markedly superior, achieving an F1-score of
98.8% compared to the 93.4% highest score from the standard VGGNet16.
Downloads
References
K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778, 2016.
D. Han, Q. Liu, and W. Fan, A new image classification method using CNN transfer learning and web data augmentation. Expert Systems with Applications, 95, 43-56, 2018.
K. Hanbay, L. Si̇vri̇kaya, and D. Hanbay, Classification of Apricot Diseases by using Deep Convolution Neural Network. BEU Journal of Science, 9(1), 2020.
N. Kumar, P. Belhumeur, A. Biswas, D. Jacobs, W.J. Kress, I. Lopez, and J. Soares, LNCS 7573 - Leafsnap: A Computer Vision System for Automatic Plant Species Identification, 2012.
G. Altan, Performance Evaluation of Capsule Networks for Classification of Plant Leaf Diseases. International Journal of Applied Mathematics Electronics and Computers, 8(3), 57-63, 2020.
Ai A. Kadir, L. Edi Nugroho, A. Susanto, and P. Insap Santosa, Leaf Classification Using Shape, Color, and Texture Features, International Journal of Computer Trends and Technology, 2011.
Vg1 L.A. Gatys, A.S. Ecker, and M. Bethge, Image style transfer using convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2414–2423, 2016.
Vg2 O. Ronneberger, P. Fischer, and T. Brox, U-Net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 234–241, 2015.
A. Canziani, A. Paszke, and E. Culurciello, An analysis of deep neural network models for practical applications, 2017. arXiv. https://arxiv.org/abs/1605.07678.
S. Han, J. Pool, J. Tran, and W.J. Dally, Learning both weights and connections for efficient neural networks. Advances in Neural Information Processing Systems (NIPS), 28.
S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, Plant disease detection using deep learning, 24th Telecommunications Forum (TELFOR), pp. 1-4, 2016.
K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, Proceedings of the International Conference on Learning Representations (ICLR). http://arxiv.org/abs/1409.1556, 2014.
V. Nair and G. Hinton, Rectified Linear Units Improve Restricted Boltzmann Machines, 2010.
S. Lee, C. Chan, S. Mayo and P. Remagnino, How deep learning extracts and learns leaf features for plant classification, Pattern Recognition, 71, 1-13, 2017.
S. Metlek, Disease Detection From Cassava Leaf Images With Deep Learning Methods In Web Environment. International Journal of 3D Printing Technologies and Digital Industry, 5(3), 2021.
E. Prasetyo, N. Suciati, C. Fatichah, Multi-level residual network VGGNet for fish species classification, Journal of King Saud University Computer and Information Sciences, 34(8), pp. 5286-5295, 2022.
W. Yu, K. Yang, Y. Bai, T. Xiao, H. Yao, and Y. Rui, “Visualizing and comparing AlexNet and VGG using deconvolutional layers”, In Proceedings of the 33 rd International Conference on Machine Learning, 3, pp. 43-76, 2016.
