| Paper title: |
Leaf Classification Using Machine Learning Algorithms |
| DOI: | https://doi.org/10.4316/JACSM.202201003 |
| Published in: | Issue 1, (Vol. 16) / 2022 |
| Publishing date: | 2022-04-05 |
| Pages: | 18-23 |
| Author(s): | SULTANA Zinnia, RAHMAN Mohammed Mahmudur, NAHAR Lutfun |
| Abstract. | On earth plants play an essential role for all living beings. A part of plants is leaf. Plant leaves are classified technically. Different morphological features has been supported for leaf assessment & classification. Some successful classification techniques are Linear Regression, KNearest Neighbor Classifier, Support Vector machine and so on. To find the standardness of the results various data sets have been used which are completely different. That's why picking the strategy for classification is not an easy task at all. Biology, Ayurveda, Agriculture etc are various fields where plant leaf classifications are used. In this paper, three datasets have been collected to classify leaf. Here the leaf images were extracted by applying relevant leaf image extraction methods. In the extracted images, there are three values which are margin, shape and texture. Then the dataset is preprocessed and different machine learning classifiers are trained. Finally, the classification test is carried out to achieve leaf classification accuracy and efficiency. Based on accuracy ,comparingwith three datasets Random Forest classifier performs better among different machine learning methods |
| Keywords: | Leaf Classification, SVM, Random Forest Classifier, Pipeline, StandardScaler |
| References: | 1. N. Goyal, N. Kumar and Kapil, "On solving leaf classification using linear regression", Multimedia Tools and Applications, vol. 80, no. 3, pp. 4533-4551, 2020 2. N. Goyal, K. Gupta and N. Kumar, "Multiclass Twin Support Vector Machine for plant species identification", Multimedia Tools and Applications, vol. 78, no. 19, pp. 27785-27808, 2019 3. A. Salman, A. Semwal, U. Bhatt and V. Thakkar, "Leaf classification and identification using Canny Edge Detector and SVM classifier", 2017 International Conference on Inventive Systems and Control (ICISC), 2017 4. L. Yang, J. Ding, L. Jiang, R. Han, Y. Bi and S. Zheng, "A Novel Method for Leaf Recognition Based on D-LLE and Polar Coordinate Feature Extraction", 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE), 2020 5. Y. Zhang, J. Jianxiong Tang, and Y. Yi Wang, “Research on plant leaf classification and retrieval method based on machine learning- iopscience.iop.org”, 2021 6. R. Akter and M. Hosen, "CNN-based Leaf Image Classification for Bangladeshi Medicinal Plant Recognition", IEEE, 2021 7. P. M. Kumar, M. Kamble, S. Pawar, P. Patil, and N. Bonde, “Survey on Techniques for Plant Leaf Classification - CITESEERX,” 2011 8. Y. Zhang, J. Peng, X. Yuan, L. Zhang, D. Zhu, P. Hong, J. Wang, Q. Liu, and W. Liu, “MFCIS: An automatic leaf-based identification pipeline for plant cultivars using Deep Learning and persistent homology,” Nature News, 2021 9. K. KC, Z. Yin, M. Wu, and Z. Wu, “Depthwise separable convolution architectures for Plant Disease Classification,” Computers and Electronics in Agriculture, 2019 10. V. Metre and J. Ghorpade, “An overview of the research on texture based plant leaf classification,”arXiv.org, 2013 11. Ç. TURHAL, “Plant identification via leaf classification using color and biometric features,” ISPEC Journal of Agricultural Science, 2021 12. K. Pankaja and V. Suma, “Plant Leaf recognition and classification based on the whale optimization algorithm (WOA) and Random Forest (RF) - journal of the Institution of Engineers (India): Series B,” SpringerLink, 2020 13. P. Bonissone, J. M. Cadenas, M. C. Garrido, and R. A. Díaz-Valladares, “A fuzzy random forest,” International Journal of Approximate Reasoning, 2010 14. B. S. Ghyar and G. K. Birajdar, “Computer Vision based approach to detect rice leaf diseases using texture and color descriptors,” IEEE Xplore, 2018 15. J. R. Rodríguez-Pérez, C. Ordóñez, A. B. González-Fernández, E. Sanz-Ablanedo, J. B. Valenciano, and V. Marcelo, “Leaf water content estimation by functional linear regression of field spectroscopy data,” Biosystems Engineering, 2017 |
| Back to the journal content | |
