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 dataset and applied class activation mapping (CAM) aggregation to investigate diagnostic keys for identifying each species. CNNs could identify the barks of 42 species with > 90% accuracy, and the overall accuracies showed a small difference between the two models. Diagnostic keys matched with salient shapes, which were also easily recognized by human eyes, and were typified as blisters, horizontal and vertical stripes, lenticels of various shapes, and vertical crevices and clefts. The two models exhibited disparate quality in the diagnostic features: the old and less complex model showed more general and well-matching patterns, while the better-performing model with much deeper layers indicated local patterns less relevant to barks. CNNs were also capable of predicting untrained species by 41.98% and 48.67% within the correct genus and family, respectively. Our methodologies and findings are potentially applicable to identify and visualize crucial traits of other plant organs.

Plant phenotyping relevance

CNNとCAMを用いて樹皮画像から識別に有用な形態的特徴を抽出・可視化する方法が研究の中心であり、植物器官の観察可能な形質の推定に該当する。

Code and data availability

The paper's own bark image dataset (BARK-KR) is publicly deposited on Zenodo, the authors' analysis scripts are on GitHub, and the CAM extended figures are hosted on Figshare. The BarkNet 1.0 dataset is cited prior work and excluded.