Unmanned aerial vehicle–light detection and ranging (UAV-LiDAR) provides a convenient and economical means of forest data acquisition that can penetrate canopy gaps to obtain abundant ground information, offering huge potential in forest inventory. Individual tree segmentation is a prerequisite to obtain individual tree details but is highly dependent on the accuracy of seed point detection. However, most of the existing methods, such as the local maximum (LM) and CHM-based methods, are strongly dependent on the window size, and, for individual tree segmentation, they can result in over-segmentation and under-segmentation, especially in natural forests. In this paper, we propose an adaptive crown shaped algorithm for individual tree segmentation without consideration of the window size. It was implemented in four plots with different forest types and topographies (i.e., planted coniferous forest with flat terrain, coniferous forest with sloping terrain, mixed forest with flat terrain and broadleaf forest with flat terrain). First, the normalized point clouds were rotated and blocked at multiple angles to extract the surface points of the forest. Then, the crown boundaries were delineated by analyzing the crown profiles to extract the treetops as seed points. Finally, a region growing method based on seed points was applied for individual tree segmentation. Our results showed that the recall, precision and F1-score of seed point detection reached 91.6%, 95.9% and 0.94, respectively, and that the accuracy rates for individual tree segmentation for the four plots were 87.7%, 80.6%, 73.2% and 70.5%, respectively. Our proposed method can effectively detect seed points via the adaptive crown shaped algorithm and reduce the impacts of elongated branches by applying distance thresholds between trees, enhancing the accuracy of seed point detection and subsequently improving the precision of individual tree segmentation. In addition, the proposed algorithm demonstrated superior performance in comparison to LM and CHM-based methods for the calculation of seed points, as well as outperforming PCS in individual tree segmentation. The proposed method demonstrates effectiveness and feasibility in dense forests and natural forests, providing an important reference for future research on seed point detection and individual tree segmentation.

Ear height (EH) and ear–plant height ratio (ER) are important agronomic traits in maize that directly affect nutrient utilization efficiency and lodging resistance and ultimately relate to maize yield. However, challenges in executing large-scale EH and ER measurements severely limit maize breeding programs. In this paper, we propose a novel, simple method for field monitoring of EH and ER based on the relationship between ear position and vertical leaf area profile. The vertical leaf area profile was estimated from Terrestrial Laser Scanner (TLS) and Drone Laser Scanner (DLS) data by applying the voxel-based point cloud method. The method was validated using two years of data collected from 128 field plots. The main factors affecting the accuracy were investigated, including the LiDAR platform, voxel size, and point cloud density. The EH using TLS data yielded R² = 0.59 and RMSE = 16.90 cm for 2019, R² = 0.39 and RMSE = 18.40 cm for 2021. In contrast, the EH using DLS data had an R² = 0.54 and RMSE = 18.00 cm for 2019, R² = 0.46 and RMSE = 26.50 cm for 2021 when the planting density was 67,500 plants/ha and below. The ER estimated using 2019 TLS data has R² = 0.45 and RMSE = 0.06. In summary, this paper proposed a simple method for measuring maize EH and ER in the field, the results will also offer insights into the structure-related traits of maize cultivars, further aiding selection in molecular breeding.

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°, 6.17°, and 19.04% and 7.95°, 10.00°, and 20.24%, respectively; and those from the structure-based method were 6.22°, 7.47°, and 23.30% and 8.99°, 12.57°, 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°, 8.12°, and 25.90% and 11.30°, 13.52°, 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.

Distributions of leaf base and inclination angles are important crop phenotypic traits, influencing light interception and productivity. Light detection and ranging (LiDAR), and especially Terrestrial laser scanning (TLS), provides unprecedented detail of the three-dimensional (3-D) structure of the crop canopy. Recent research mainly focuses on the leaf base and inclination angles of maize at the individual level or at lower planting density. It is difficult to extract the distributions of leaf base and inclination angles of maize in the field due to the interlocked and overlapped nature of leaves. In this study, we have proposed a high-throughput method to extract the distributions of leaf base and inclination angles of maize in the field. Following the separation of the leaf and stem of maize, hollow cylinders with different thicknesses were used to extract the local leaf points from the separated leaf points based on each stem fitted line, and the Density-based spatial clustering of applications with noise (DBSCAN) algorithm and singular value decomposition were used to calculate the leaf base and inclination angles. The distributions of leaf base and inclination angles of maize in the field with different cultivars (Jingjiuqingchu 16 (A1), Tianci 19 (A2), Jingnuo 2008 (A3), Nongkenuo 336 (A4), and Zhengdan 958 (A5)), planting densities (3.32 plants/m ² , 4.65 plants/m ² , 6.64 plants/m ² , and 8.63 plants/m ² ), and growth stages (jointing, silking, and filling stages) were extracted and analyzed, and these performed well against the validation data. In addition to TLS data, the extraction of the distributions of leaf base and inclination angles based on LiBackpack and UAV-LiDAR data was also discussed. This further validated the potential of the method proposed in this study for the extraction of the distributions of leaf base and inclination angles of maize in the field. Furthermore, the relationship between maize with different leaf base angle distributions and the daily cumulative APAR (Absorbed Photosynthetically Active Radiation) was analyzed, which demonstrated that compact maize cultivars exhibited higher light interception capabilities than scattered ones under high planting densities. The distributions of leaf base and inclination angles exert a substantial influence on the light interception capacity of maize, thereby exerting a consequential effect on maize yield. The high-throughput extraction of these distributions in maize fields holds significant importance for studying the optimal maize cultivar in conjunction with radiative transfer models.

The accurate and high-throughput extraction of phenotypic traits is of great significance for crop breeding and growth monitoring. The segmentation of structural components (e.g., leaves and stems) is a prerequisite for extracting phenotypic traits. In the past decade, there has been an increase in methods attempting to separate leaves and stems in point clouds. However, previous researches mainly focus on plants at the individual level due to the interlocked and overlapped nature of leaves and the bottleneck existing for field plants to extract phenotypic traits. To address this issue, a novel two-stage leaf–stem separation model encompassing the initial separation of leaves and stems and optimization is presented in this article. The model is based on the different geometric features of leaves and stems of maize plants defined by neighborhood points, and a cylinder is used to find the neighborhood points by considering the elongated characteristic of maize stems. After that, another elongated cylinder (0.5 m high and 0.02 m diameter) is used to traverse the stem points to optimize the initially separated results. Maize plants with the planting density of 45 000 plants/ha in the filling stage (Exp. 2019) were used to train and test the model in the initial separation step (Experiment 1), showing that the separation accuracy (SA) could be up to 91.3%. It was concluded that a 0.11-m-high and 0.07-m diameter cylinder was the optimal searching parameter for the initial separation and 0.25 m was the optimal threshold for optimization. We also tested the transferability of the model (Experiment 2) for maize plants with different planting densities (45 000, 67 500, 90 000, and 105 000 plants/ha), different growth stages (jointing, silking and filling), and point clouds collected using multiple platforms [terrestrial laser scanning (TLS), light detection and ranging (LiDAR) Backpack (LiBackpack), and unmanned aerial vehicle-LiDAR (UAV-LiDAR)], suggesting that the model performed well for all the datasets. In addition, the simulated datasets of maize with different planting densities were used to assess the model performance at the point level, showing the SA values were 0.92, 0.91, 0.91, and 0.90 for maize with the planting densities of 45 000, 67 500, 90 000, and 105 000 plants/ha, respectively. The proposed model in this study is innovative, and it has promising prospects for the high-throughput extraction of the phenotypic traits in field maize plants and could facilitate genotype selection in crop breeding and 3-D plant modeling.