DeepLeaf: Automated Leaf Classification Using Convolutional Neural Networks

Najla Althuniyan; Ala’a R. Al-Shamasneh; Arwa Bawazir; Zainab Mohiuddin; Shroug Bawazir

doi:10.19044/esj.2024.v20n30p22

Najla Althuniyan Dept. of Computer Science, Prince Sultan University, Kingdome of Saudi Arabia, Riyadh
Ala’a R. Al-Shamasneh Dept. of Computer Science, Prince Sultan University, Kingdome of Saudi Arabia, Riyadh
Arwa Bawazir Dept. of Computer Science, Prince Sultan University, Kingdome of Saudi Arabia, Riyadh
Zainab Mohiuddin Dept. of Computer Science, Prince Sultan University, Kingdome of Saudi Arabia, Riyadh
Shroug Bawazir Dept. of Computer Science, Prince Sultan University, Kingdome of Saudi Arabia, Riyadh

DOI: https://doi.org/10.19044/esj.2024.v20n30p22

Keywords: Convolutional Neural Networks (CNNs), Deep learning, Preprocessing, Leaf classification

Abstract

This paper presents a methodology for automated classification of leaves using Convolutional Neural Networks (CNNs). Leaf classification plays a crucial role in various domains such as agriculture, botany, and environmental science. Traditional methods for leaf classification often rely on manual feature extraction and handcrafted classifiers, which can be time-consuming and limited in their accuracy. In this work, we propose a deep learning approach that leverages the power of CNNs to automatically learn discriminative features from leaf images. The proposed framework consists of several key stages: preprocessing, data augmentation, model architecture design, training, and evaluation. The leaf images are preprocessed to enhance quality and normalize dimensions. Data augmentation techniques are applied to increase the diversity of the training dataset and improve the generalization capability of the model. The CNN architecture is carefully designed to effectively capture hierarchical features present in leaf images. We train the CNN using a large dataset of labeled leaf images, employing techniques such as transfer learning to utilize pre-trained models to optimize training efficiency.. The trained model is evaluated using various metrics such as accuracy, precision, recall, and F1 score on a separate test dataset. The experimental results showcase the proposed approach's effectiveness in accurately classifying various leaf types. Overall, this study showcases the promising capabilities of deep learning techniques for automated leaf classification, paving the way for advanced applications in plant biology and agriculture.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

PlumX Statistics

References

1. Abade, A., Ferreira, P. A., & Barata, C. (2021). Plant leaf identification using deep learning models. Computers in Biology and Medicine, 133, 104403.
2. Amara, J., Bouaziz, B., & Algergawy, A. (2017). A deep learning-based approach for banana leaf diseases classification. In BTW (pp. 79-88).
3. Barbedo, J. G. A. (2016). A review on the main challenges in automatic plant disease identification based on visible range images. Biosystems Engineering, 144, 52-60.
4. Choudhury, S., Samanta, S., & Sil, J. (2022). A comprehensive review on deep learning techniques for plant disease detection. Journal of Ambient Intelligence and Humanized Computing, 1-18.
5. Dyrmann, M., Karstoft, H., & Midtiby, H. S. (2016). Plant species classification using deep convolutional neural networks. Biosystems Engineering, 151, 72-80.
6. Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311-318.
7. Ghosal, S., Blystone, D., Singh, A. K., Ganapathysubramanian, B., Singh, A., & Sarkar, S. (2018). An explainable deep machine vision framework for plant stress phenotyping. Proceedings of the National Academy of Sciences, 115(18), 4613-4618.
8. Kumar, A., Jatav, N. K., & Singh, R. (2021). Deep learning-based automatic plant disease detection: A review. Archives of Computational Methods in Engineering, 1-21.
9. Liu, B., Zhang, Y., He, D., & Li, Y. (2017). Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 9(8), 10.
10. Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419.
11. Nagasubramanian, K., Jones, S., Sarkar, S., Singh, A. K., Singh, A., & Ganapathysubramanian, B. (2019). Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant Methods, 15, 98.
12. Patel, R., & Jain, D. (2019). Leaf disease detection using deep learning techniques: A review. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
13. Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J., & Johannes, A. (2019). Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Computers and Electronics in Agriculture, 161, 280-290.
14. Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., & Hughes, D. P. (2017). Deep learning for image-based cassava disease detection. Frontiers in Plant Science, 8, 1852.
15. Singh, V., & Misra, A. K. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4(1), 41-49.
16. Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016, 1-11.
17. Smith, A. B., & Jones, C. D. (2020). DeepLeaf: A deep learning-based framework for automated plant leaf segmentation and classification. Computers and Electronics in Agriculture, 176, 105684.
18. Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161, 272-279.
19. Wang, Y., & Li, W. (2021). A comprehensive review on leaf disease detection using deep learning techniques. Computers and Electronics in Agriculture, 192, 105627.
20. Zhang, Y., Li, X., & Hu, X. (2023). Deep learning-based leaf disease detection and classification: A review. In 2023 4th International Conference on Automation, Control and Robotics Engineering (CACRE) (pp. 1-6). IEEE.