Monitoring of yellow leaf disease (YLD) damage based on ground-based LiDAR and UAV multispectral data

文献类型： 外文期刊

第一作者： Zhang, Xudong

作者： Zhang, Xudong;Bi, Peiyun;Zhou, Quan;Ren, Lili;Luo, Youqing;Liu, Li

作者机构：

关键词： LiDAR; Multispectral imaging; Data fusion; Classification of damage; Machine learning

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 236 卷

页码：

收录情况： SCI

摘要： Areca catechu L. (Arecaceae) is an important cash crop in Taiwan(China), Hainan (China), and several South Asian countries. Areca palm yellow leaf disease (YLD) poses a severe threat, leading to reduced yields and eventual plant mortality. The current study differentiates areca palm damage solely based on spectral features. We are the first to integrate LiDAR point clouds with multispectral imagery to distinguish between different damage levels. We standardized the geographic coordinate systems of the LiDAR data and multispectral images, then aligned them using the control point method. During YLD infestation, areca palm leaves turn yellow and eventually fall off. We surveyed over 1000 trees, counting the number of leaves and calculating the proportion of the canopy area covered by yellowing foliage. Based on crown color changes and leaf count, we classified areca palm damage into five levels: healthy, slightly damaged, moderately damaged, severely damaged, and dying/ dead. Meanwhile, this study optimized the individual tree segmentation process for areca palm. Using trunk point clouds, we generated seed points and applied region-growing cluster segmentation to achieve a more accurate individual tree profile compared to the traditional watershed algorithm. Based on the segmentation results and crown contours, we extracted the structural and spectral characteristics of individual trees. Multiple algorithms were then applied to classify areca palms into four damage levels: healthy, slightly damaged, moderately damaged, and severely damaged. The classification achieved an overall accuracy of 86.46% and a kappa value of 0.819. The inclusion of LiDAR data improved the overall accuracy by 23.94% compared to using only spectral features. Comparatively, past studies have relied only on spectral differences to determine the area of leaf yellowing and thus further determine the level of damage. In this study, we further noted the structural changes in the canopy caused by leaf abscission, provided a more realistic description of the different damage levels, and constructed more accurate models. The proposed method demonstrates great potential in YLD damage classification and provides an important basis for precise management of plantations.

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