Common Bamboo Species Identification using Machine Learning and Deep Learning Algorithms
Piyush Juyal1, Chitransh Kulshrestha2, Sachin Sharma3, Tejasvi Ghanshala4
1Piyush Juyal*, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.
2Chitransh Kulshrestha, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.
3Sachin Sharma, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
4Tejasvi Ghanshala, Faculty of Applied Science, The University of British Columbia, Vancouver, Canada
Manuscript received on January 13, 2020. | Revised Manuscript received on January 21, 2020. | Manuscript published on February 10, 2020. | PP: 3012-3017 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1609029420/2020©BEIESP | DOI: 10.35940/ijitee.D1609.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Due to its growth rate and strength, bamboo’s versatility is huge. Bamboo has been developed to replace hardwood naturally. But it can be difficult to recognize a bamboo as many appear in a cluster or singular. Each bamboo type has its applications. Because of the utility of bamboo, we have worked in Random Forest, naive bays, logistic regression, the SVM-kernel, CNN, and ResNET, amongst several machine-learning algorithms. A similar test was carried out and delineated using graphs based on uncertainty matrix parameters and training accuracy. In this paper, we have used the data of following five species such as Phyllostachys nigra, Bambusa vulgaris ‘Striata‘, Dendrocalamus giganteu, Bambusa ventricosa, and Bambusa tulda which are generally found in north India. We trained, tested and validated the species from datasets using different machine learning and deep learning algorithms.
Keywords: Machine learning, Random forest, naive Bayes, logistic regression, kernel SVM, CNN and ResNet
Scope of the Article: Machine learning,