Identifying and extracting bark key features of 42 tree species using convolutional neural networks and class activation mapping.

ABSTRACT: The significance of automatic plant identification has already been recognized by academia and industry. There were several attempts to utilize leaves and flowers for identification; however, bark also could be beneficial, especially for trees, due to its consistency throughout the seasons and its easy accessibility, even in high crown conditions. Previous studies regarding bark identification have mostly contributed quantitatively to increasing classification accuracy. However, ever since computer vision algorithms surpassed the identification ability of humans, an open question arises as to how machines successfully interpret and unravel the complicated patterns of barks. Here, we trained two convolutional neural networks (CNNs) with distinct architectures using a large-scale bark image

SUBMITTER: Kim TK 

PROVIDER: S-EPMC8934343 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature