Extraction of Maize Leaf Base and Inclination Angles Using Terrestrial Laser Scanning (TLS) Data

文献类型： 外文期刊

第一作者： Lei, Lei

作者： Lei, Lei;Li, Zhenhong;Lei, Lei;Li, Zhenhong;Lei, Lei;Li, Zhenhong;Wu, Jintao;Zhang, Chengjian;Zhu, Yaohui;Chen, Riqiang;Dong, Zhen;Yang, Hao;Yang, Guijun

作者机构：

关键词： Point cloud compression; Agriculture; Skeleton; Learning systems; Software; Information technology; Geologic measurements; Leaf base angle; leaf inclination angle; machine learning-based method; structure-based method; terrestrial laser scanning (TLS)

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：8.125； 五年影响因子：8.137 ）

ISSN： 0196-2892

年卷期： 2022 年 60 卷

页码：

收录情况： SCI

摘要： Leaf base and inclination angles are two critical 3-D structural parameters in agronomy and remote sensing for breeding and modeling. Terrestrial laser scanning (TLS) has been proven to be a promising tool to quantify leaf base and inclination angles. However, previous TLS studies often focused on leaf base and inclination angles of certain trees or plants with flat leaves, such as European beech. Few studies have worked on leaf base and inclination angles of maize plants due to their curved and elongated characteristics. In this study, a machine learning-based [support vector machine (SVM)] method and a structure-based [skeleton extraction (SE)] method were presented to extract the leaf base and inclination angles of maize plants. After separating individual leaf points from the complete point cloud and skeleton points of maize plants and then extracting geometric features, the machine learning- and structure-based methods were used to calculate leaf base and inclination angles. Our results show that the leaf base and inclination angles extracted using these two methods agreed well with ground truth, and the estimation accuracy of the machine learning-based method was obviously higher than that of the structure-based method. The mean absolute error (MAE), root-mean-squared error (RMSE), and relative RMSE (rRMSE) of the leaf base and inclination angles using the machine learning-based method were 4.56 degrees, 6.17 degrees, and 19.04% and 7.95 degrees, 10.00 degrees, and 20.24%, respectively; and those from the structure-based method were 6.22 degrees, 7.47 degrees, and 23.30% and 8.99 degrees, 12.57 degrees, and 25.85%, respectively. The machine learning-based method was also applied to a field with dense mature maize, and their MAE, RMSE, and rRMSE were 6.04 degrees, 8.12 degrees, and 25.90% and 11.30 degrees, 13.52 degrees, and 26.5%, respectively. It is demonstrated that both the machine learning- and structure-based methods are effective to estimate the leaf base and inclination angles of maize plants, although the machine learning-based method appears to outperform the structure-based method.

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