A ground-based scanning lidar (light detection and ranging) system was evaluated to assess its potential utility for tree-level forest mensuration data extraction. Ground-based-lidar and field-mensuration data were collected for two forest plots: one located within a red pine (Pinus resinosa Ait.) plantation and another in a mixed deciduous stand dominated by sugar maple (Acer saccharum Marsh.). Five lidar point cloud scans were collected from different vantage points for each plot over a 6-h period on 5 July 2002 using an Optech Inc. ILRIS-3D laser imager. Field- validation data were collected manually over several days during the same time period. Parameters that were measured in the field or derived from manual field measures included (i) stem location, (ii) tree height, (iii) stem diameter at breast height (DBH), (iv) stem density, and (v) timber volume. These measures were then compared with those derived from the ILRIS-3D data (i.e., the lidar point cloud data). It was found that all parameters could be measured or derived from the data collected by the ground-based lidar system. There was a slight systematic underestimation of mean tree height resulting from canopy shadow effects and suboptimal scan sampling distribution. Timber volume estimates for both plots were within 7% of manually derived estimates. Tree height and DBH parameters have the potential for objective measurement or derivation with little manual intervention. However, locating and counting trees within the lidar point cloud, particularly in the multitiered deciduous plot, required the assistance of field-validation data and some subjective interpretation. Overall, ground-based lidar demonstrates promise for objective and consistent forest metric assessment, but work is needed to refine and develop automatic feature identification and data extraction techniques.

Leaf area index and leaf area density profiles are key variables for upscaling from leaves to ecosystems yet are difficult to measure well in dense and tall forest canopies. We present a new model to estimate leaf area density profiles from discrete multireturn data derived by airborne waveform light detection and ranging (lidar), a model based on stochastic radiative transfer theory. We tested the method on simulated ray tracing data for highly clumped forest canopies, both vertically homogenous and vertically inhomogeneous. Our method was able to reproduce simulated vertical foliage profiles with small errors and predictable biases in dense canopies (leaf area index = 6) including layers below densely foliated upper canopies. As a case study, we then applied the method to real multireturn airborne lidar data for a 50 ha plot of moist tropical forest on Barro Colorado Island, Panama. The method is suitable for estimating foliage profiles in a complex tropical forest, which opens new avenues for analyses of spatial and temporal variations in foliage distributions.

Automatic and efficient plant monitoring offers accurate plant management. Construction of three-dimensional (3D) models of plants and acquisition of their spatial information is an effective method for obtaining plant structural parameters. Here, 3D images of leaves constructed with multiple scenes taken from different positions were segmented automatically for the automatic retrieval of leaf areas and inclination angles. First, for the initial segmentation, leave images were viewed from the top, then leaves in the top-view images were segmented using distance transform and the watershed algorithm. Next, the images of leaves after the initial segmentation were reduced by 90%, and the seed regions for each leaf were produced. The seed region was re-projected onto the 3D images, and each leaf was segmented by expanding the seed region with the 3D information. After leaf segmentation, the leaf area of each leaf and its inclination angle were estimated accurately via a voxel-based calculation. As a result, leaf area and leaf inclination angle were estimated accurately after automatic leaf segmentation. This method for automatic plant structure analysis allows accurate and efficient plant breeding and growth management.

Automatic leaf segmentation, as well as identification and classification methods that built upon it, are able to provide immediate monitoring for plant growth status to guarantee the output. Although 3D plant point clouds contain abundant phenotypic features, plant leaves are usually distributed in clusters and are sometimes seriously overlapped in the canopy. Therefore, it is still a big challenge to automatically segment each individual leaf from a highly crowded plant canopy in 3D for plant phenotyping purposes. In this work, we propose an overlapping-free individual leaf segmentation method for plant point clouds using the 3D filtering and facet region growing. In order to separate leaves with different overlapping situations, we develop a new 3D joint filtering operator, which integrates a Radius-based Outlier Filter (RBOF) and a Surface Boundary Filter (SBF) to help to separate occluded leaves. By introducing the facet over-segmentation and facet-based region growing, the noise in segmentation is suppressed and labeled leaf centers can expand to their whole leaves, respectively. Our method can work on point clouds generated from three types of 3D imaging platforms, and also suitable for different kinds of plant species. In experiments, it obtains a point-level cover rate of 97% for Epipremnum aureum, 99% for Monstera deliciosa, 99% for Calathea makoyana, and 87% for Hedera nepalensis sample plants. At the leaf level, our method reaches an average Recall at 100.00%, a Precision at 99.33%, and an average F-measure at 99.66%, respectively. The proposed method can also facilitate the automatic traits estimation of each single leaf (such as the leaf area, length, and width), which has potential to become a highly effective tool for plant research and agricultural engineering.

To accelerate the understanding of the relationship between genotype and phenotype, plant scientists and plant breeders are looking for more advanced phenotyping systems that provide more detailed phenotypic information about plants. Most current systems provide information on the whole-plant level and not on the level of specific plant parts such as leaves, nodes and stems. Computer vision provides possibilities to extract information from plant parts from images. However, the segmentation of plant parts is a challenging problem, due to the inherent variation in appearance and shape of natural objects. In this paper, deep-learning methods are proposed to deal with this variation. Moreover, a multi-view approach is taken that allows the integration of information from the two-dimensional (2D) images into a three-dimensional (3D) point-cloud model of the plant. Specifically, a fully convolutional network (FCN) and a masked R-CNN (region-based convolutional neural network) were used for semantic and instance segmentation on the 2D images. The different viewpoints were then combined to segment the 3D point cloud. The performance of the 2D and multi-view approaches was evaluated on tomato seedling plants. Our results show that the integration of information in 3D outperforms the 2D approach, because errors in 2D are not persistent for the different viewpoints and can therefore be overcome in 3D. (C) 2019 IAgrE. Published by Elsevier Ltd.

In this paper, a novel point cloud segmentation and completion framework is proposed to achieve high-quality leaf area measurement of melon seedlings. In particular, the input of our algorithm is the point cloud data collected by an Azure Kinect camera from the top view of the seedlings, and our method can enhance measurement accuracy from two aspects based on the acquired data. On the one hand, we propose a neighborhood space-constrained method to effectively filter out the hover points and outlier noise of the point cloud, which can enhance the quality of the point cloud data significantly. On the other hand, by leveraging the purely linear mixer mechanism, a new network named MIX-Net is developed to achieve segmentation and completion of the point cloud simultaneously. Different from previous methods that separate these two tasks, the proposed network can better balance these two tasks in a more definite and effective way, leading to satisfactory performance on these two tasks. The experimental results prove that our methods can outperform other competitors and provide more accurate measurement results. Specifically, for the seedling segmentation task, our method can obtain a 3.1% and 1.7% performance gain compared with PointNet++ and DGCNN, respectively. Meanwhile, the R2 of leaf area measurement improved from 0.87 to 0.93 and MSE decreased from 2.64 to 2.26 after leaf shading completion.

Leaf attribute estimation is crucial for understanding photosynthesis, respiration, transpiration, and carbon and nutrient cycling in vegetation and evaluating the biological parameters of plants or forests. Terrestrial laser scanning (TLS) has the capability to provide detailed characterisations of individual trees at both the branch and leaf scales and to extract accurate structural parameters of stems and crowns. In this paper, we developed a computer graphic-based 3D point cloud segmentation approach for accurately and efficiently detecting tree leaves and their morphological features (i.e., leaf area and leaf angle distributions (leaf azimuthal angle and leaf inclination angle)) from single leaves. To this end, we adopted a sphere neighbourhood model with an adaptive radius to extract the central area points of individual leaves with different morphological structures and complex spatial distributions; meanwhile, four auxiliary criteria were defined to ensure the accuracy of the extracted central area points of individual leaf surfaces. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to cluster the central area points of leaves and to obtain the centre point corresponding to each leaf surface. We also achieved segmentation of individual leaf blades using an advanced 3D watershed algorithm based on the extracted centre point of each leaf surface and two morphology-related parameters. Finally, the leaf attributes (leaf area and leaf angle distributions) were calculated and assessed by analysing the segmented single-leaf point cloud. To validate the final results, the actual leaf area, leaf inclination and azimuthal angle data of designated leaves on the experimental trees were manually measured during field activities. In addition, a sensitivity analysis investigated the effect of the parameters in our segmentation algorithm. The results demonstrated that the segmentation accuracy of Ehretia macrophylla (94.0%) was higher than that of crape myrtle (90.6%) and Fatsia japonica (88.8%). The segmentation accuracy of Fatsia japonica was the lowest of the three experimental trees. In addition, the single-leaf area estimation accuracy for Ehretia macrophylla (95.39%) was still the highest among the three experimental trees, and the single-leaf area estimation accuracy for crape myrtle (91.92%) was lower than that for Ehretia macrophylla (95.39%) and Fatsia japonica (92.48%). Third, the method proposed in this paper provided accurate leaf inclination and azimuthal angles for the three experimental trees (Ehretia macrophylla: leaf inclination angle: R 2 = 0.908, RMSE = 6.806° and leaf azimuth angle: R 2 = 0.981, RMSE = 7.680°; crape myrtle: leaf inclination angle: R 2 = 0.901, RMSE = 8.365° and leaf azimuth angle: R 2 = 0.938, RMSE = 7.573°; Fatsia japonica: leaf inclination angle: R 2 = 0.849, RMSE = 6.158° and leaf azimuth angle: R 2 = 0.947, RMSE = 3.946°). The results indicate that the proposed method is effective and operational for providing accurate, detailed information on single leaves and vegetation structure from scanned data. This capability facilitates improvements in applications such as the estimation of leaf area, leaf angle distribution and biomass.