Aug 2026· Agronomy· Vol 16, pp. 1619· 0 citations· 38 references
Abstract
Canopy 3D architecture is a critical determinant of light interception, photosynthetic efficiency, and final yield in cotton, yet its rapid and accurate characterisation remains challenging in field conditions. To achieve efficient, non-destructive, and quantitative monitoring of the canopy structure of field-grown cotton, this study proposes a 3D structure-based technology stack for high-efficiency, low-cost, and high-precision phenotyping extraction. This stack directly addresses the technical bottlenecks of traditional 3D data acquisition, namely high cost, long processing time, and low operational efficiency, which have hindered large-scale application. We developed a pipeline that integrates a fast reconstruction algorithm with a scale-recovery mechanism using ground control points (GCPs), enabling the generation of true-scale 3D point clouds from UAV aerial images in a cost- and time-effective manner. Using only 141 UAV images and with a reconstruction time of approximately 20 min, we efficiently reconstructed high-quality, scale-accurate point clouds of two 5.5 m × 5.5 m cotton plots, significantly outperforming SfM-MVS and Instant-NGP in terms of both reconstruction efficiency and point cloud completeness. This method, whose current validation is confined to a single season, one growth stage, and two experimental plots, not only achieves a breakthrough by using fewer input images with high efficiency, but also ensures point cloud accuracy and completeness, showing strong potential for rapid field monitoring and real-time management. Based on the high-quality reconstructed point clouds, we further quantitatively evaluated canopy characteristics at harvest, analyzing the coefficient of variation of canopy height, porosity distribution, and canopy volume fraction. The core shortcomings and optimization strategies for the existing canopy structure were identified, providing scientific data support and practical technical references for precision cultivation management and mechanization-compatible planting in cotton.
Terrestrial laser scanning (TLS) enables rapid, objective, and repeatable measurement of 3D forest and shrubland structure, but operational use is often limited by complex, multi-stage processing workflows. IntELiMon (Interagency Ecosystem lidar Monitoring) is a flexible, automated processing system that streamlines...
S. Pokswinski, Kurtis Nelson, Aaron E. Maxwell et al.· Forestry: An International J...· 0 citations
The integration of LiDAR point clouds acquired from airborne, terrestrial, and mobile platforms can improve the characterization of forest spatial structure. However, robust cross-platform registration remains challenging because of occlusion, viewpoint differences, uneven point density, and the limited repeatability o...
Jian You, Yun-Cheng Deng, Jia Shi et al.· IEEE Journal of Selected Top...· 0 citations
These results demonstrate that combining mobile devices with open data enables non-experts to construct high-accuracy 3D maps suitable for disaster risk management applications.
Ryosei Ueda, Daisuke Yoshida· The International Archives o...· 0 citations
Introduction Accurate extraction of maize seedling phenotypes is essential for early growth assessment, yet field-grown seedlings exhibit complex backgrounds, non-rigid leaf deformation, self-occlusion, and adhered leaves that challenge three-dimensional point-cloud analysis. Methods Here, we developed a workflow integ...
Jing Zhou, Wen-Long Zou, Yu-Shan Wu et al.· Frontiers in Plant Science· 0 citations
Understanding current forest resource status is fundamental for formulating forestry policies and management plans. 3D laser scanning provides an effective approach for conducting forest resource surveys efficiently and accurately, which is the core background and aim of this study. This research used Unmanned Aerial V...
Yong-Wei Wu, Yi-Ming Zhang, Kai Su et al.· Land· 0 citations
A scale-consistent framework that integrates UAV-LiDAR, three-dimensional simulation, multisource remote sensing, and machine learning for Chinese fir (Cunninghamia lanceolata) plantation AGB estimation is developed, highlighting its potential for scalable and reliable forest biomass estimation in data-limited subtropi...