Common Bamboo Species Identification using Machine Learning and Deep Learning Algorithms

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.