Plant Population Classification Based on PointCNN in the Daliyabuyi Oasis, China

Abstract

Populus euphratica and Tamarix chinensis hold significant importance in wind prevention, sand fixation, and biodiversity conservation. The precise extraction of these species can offer technical assistance for vegetation studies. This paper focuses on the Populus euphratica and Tamarix chinensis located within Daliyabuyi, utilizing PointCNN as the primary research method. After decorrelating and stretching the images, deep learning techniques were applied, successfully distinguishing between various vegetation types, thereby enhancing the precision of vegetation information extraction. On the validation dataset, the PointCNN model showcased a high degree of accuracy, with the respective regular accuracy rates for Populus euphratica and Tamarix chinensis being 92.106% and 91.936%. In comparison to two-dimensional deep learning models, the classification accuracy of the PointCNN model is superior. Additionally, this study extracted individual tree information for the Populus euphratica, such as tree height, crown width, crown area, and crown volume. A comparative analysis with the validation data attested to the accuracy of the extracted results. Furthermore, this research concluded that the batch size and block size in deep learning model training could influence classification outcomes. In summary, compared to 2D deep learning models, the point cloud deep learning approach of the PointCNN model exhibits higher accuracy and reliability in classifying and extracting information for poplars and tamarisks. These research findings offer valuable references and insights for remote sensing image processing and vegetation study domains.

Plant phenotyping relevance

PointCNNによる点群解析を中核として、個体樹木の樹高・樹冠幅・樹冠面積・樹冠体積を抽出し、検証データで精度評価しているため、植物表現型取得手法の開発・検証に該当する。

Code and data availability

The supplied blocks describe UAV imagery, point clouds, and a PointCNN classification workflow, but contain no data availability statement, no public repository deposit, and no author-provided URL for datasets, code, or trained models. No paper-specific public asset is actionable.

No evidence-backed public reproduction asset is currently recorded.