3-D Modeling of Tomato Canopies Using a High-Resolution Portable Scanning Lidar for Extracting Structural Information

In the present study, an attempt was made to produce a precise 3D image of a tomato canopy using a portable high-resolution scanning lidar. The tomato canopy was scanned by the lidar from three positions surrounding it. Through the scanning, the point cloud data of the canopy were obtained and they were co-registered. Then, points corresponding to leaves were extracted and converted into polygon images. From the polygon images, leaf areas were accurately estimated with a mean absolute percent error of 4.6%. Vertical profile of leaf area density (LAD) and leaf area index (LAI) could be also estimated by summing up each leaf area derived from the polygon images. Leaf inclination angle could be also estimated from the 3-D polygon image. It was shown that leaf inclination angles had different values at each part of a leaf

Estimating rice yield related traits and quantitative trait loci analysis under different nitrogen treatments using a simple tower-based field phenotyping system with modified single-lens reflex cameras

CIAT- Outstanding Research Publication Award (ORPA) - 2017Application of field based high-throughput phenotyping (FB-HTP) methods for monitoring plant performance in real field conditions has a high potential to accelerate the breeding process. In this paper, we discuss the use of a simple tower based remote sensing platform using modified single-lens reflex cameras for phenotyping yield traits in rice under different nitrogen (N) treatments over three years. This tower based phenotyping platform has the advantages of simplicity, ease and stability in terms of introduction, maintenance and continual operation under field conditions. Out of six phenological stages of rice analyzed, the flowering stage was the most useful in the estimation of yield performance under field conditions. We found a high correlation between several vegetation indices (simple ratio (SR), normalized difference vegetation index (NDVI), transformed vegetation index (TVI), corrected transformed vegetation index (CTVI), soil-adjusted vegetation index (SAVI) and modified soil-adjusted vegetation index (MSAVI)) and multiple yield traits (panicle number, grain weight and shoot biomass) across a three trials. Among all of the indices studied, SR exhibited the best performance in regards to the estimation of grain weight (R2 = 0.80). Under our tower-based field phenotyping system (TBFPS), we identified quantitative trait loci (QTL) for yield related traits using a mapping population of chromosome segment substitution lines (CSSLs) and a single nucleotide polymorphism data set. Our findings suggest the TBFPS can be useful for the estimation of yield performance during early crop development. This can be a major opportunity for rice breeders whom desire high throughput phenotypic selection for yield performance traits

Automatic Leaf Segmentation for Estimating Leaf Area and Leaf Inclination Angle in 3D Plant Images

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

Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar

The leaf inclination angle is a fundamental variable for determining the plant profile. In this study, the leaf inclination angle was estimated automatically from voxel-based three-dimensional (3D) images obtained from lidar (light detection and ranging). The distribution of the leaf inclination angle within a tree was then calculated. The 3D images were first converted into voxel coordinates. Then, a plane was fitted to some voxels surrounding the point (voxel) of interest. The inclination angle and azimuth angle were obtained from the normal. The measured leaf inclination angle and its actual value were correlated and indicated a high correlation (R2 = 0.95). The absolute error of the leaf inclination angle estimation was 2.5°. Furthermore, the leaf inclination angle can be estimated even when the distance between the lidar and leaves is about 20 m. This suggests that the inclination angle estimation of leaves in a top part is reliable. Then, the leaf inclination angle distribution within a tree was calculated. The difference in the leaf inclination angle distribution between different parts within a tree was observed, and a detailed tree structural analysis was conducted. We found that this method enables accurate and efficient leaf inclination angle distribution

Three-Dimensional Monitoring of Plant Structural Parameters and Chlorophyll Distribution

Image analysis is widely used for accurate and efficient plant monitoring. Plants have complex three-dimensional (3D) structures; hence, 3D image acquisition and analysis is useful for determining the status of plants. Here, 3D images of plants were reconstructed using a photogrammetric approach, called “structure from motion„. Chlorophyll content is an important parameter that determines the status of plants. Chlorophyll content was estimated from 3D images of plants with color information. To observe changes in the chlorophyll content and plant structure, a potted plant was kept for five days under a water stress condition and its 3D images were taken once a day. As a result, the normalized Red value and the chlorophyll content were correlated; a high R2 value (0.81) was obtained. The absolute error of the chlorophyll content estimation in cross-validation studies was 4.0 × 10−2 μg/mm2. At the same time, the structural parameters (i.e., the leaf inclination angle and the azimuthal angle) were calculated by simultaneously monitoring the changes in the plant’s status in terms of its chlorophyll content and structural parameters. By combining these parameters related to plant information in plant image analysis, early detection of plant stressors, such as water stress, becomes possible

Leaf Segmentation Based on k-Means Algorithm to Obtain Leaf Angle Distribution Using Terrestrial LiDAR

It is critical to take the variability of leaf angle distribution into account in a remote sensing analysis of a canopy system. Due to the physical limitations of field measurements, it is difficult to obtain leaf angles quickly and accurately, especially with a complicated canopy structure. An application of terrestrial LiDAR (Light Detection and Ranging) is a common solution for the purposes of leaf angle estimation, and it allows for the measurement and reconstruction of 3D canopy models with an arbitrary volume of leaves. However, in most cases, the leaf angle is estimated incorrectly due to inaccurate leaf segmentation. Therefore, the objective of this study was an emphasis on the development of efficient segmentation algorithms for accurate leaf angle estimation. Our study demonstrates a leaf segmentation approach based on a k-means algorithm coupled with an octree structure and the subsequent application of plane-fitting to estimate the leaf angle. Furthermore, the accuracy of the segmentation and leaf angle estimation was verified. The results showed average segmentation accuracies of 95% and 90% and absolute angular errors of 3° and 6° in the leaves sampled from mochi and Japanese camellia trees, respectively. It is our conclusion that our method of leaf angle estimation has high potential and is expected to make a significant contribution to future plant and forest research