scholarly journals High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform

2019 ◽  
Vol 11 (9) ◽  
pp. 1085 ◽  
Author(s):  
Xiaodan Ma ◽  
Kexin Zhu ◽  
Haiou Guan ◽  
Jiarui Feng ◽  
Song Yu ◽  
...  
Keyword(s):  
Plant Height ◽  
High Throughput ◽  
Point Cloud ◽  
Canopy Structure ◽  
Canopy Development ◽  
Depth Images ◽  
Color Indices ◽  
High Throughput Phenotyping ◽  
Soybean Canopy ◽  
Soybean Plants

Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R2 values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W–E) direction, and canopy breadth in the north-south (N–S) direction, and R2 values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs.

scholarly journals High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates

2017 ◽  
Vol 8 ◽  
Author(s):  
Simon Madec ◽  
Fred Baret ◽  
Benoît de Solan ◽  
Samuel Thomas ◽  
Dan Dutartre ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Plant Height ◽  
High Throughput ◽  
Aerial Vehicles ◽  
High Throughput Phenotyping

scholarly journals In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR

2017 ◽  
Vol 9 (4) ◽  
pp. 377 ◽  
Author(s):  
Shangpeng Sun ◽  
Changying Li ◽  
Andrew Paterson
Keyword(s):  
Plant Height ◽  
Cotton Plant ◽  
High Throughput ◽  
High Throughput Phenotyping

scholarly journals An Efficient Processing Approach for Colored Point Cloud-Based High-Throughput Seedling Phenotyping

2020 ◽  
Vol 12 (10) ◽  
pp. 1540 ◽  
Author(s):  
Si Yang ◽  
Lihua Zheng ◽  
Wanlin Gao ◽  
Bingbing Wang ◽  
Xia Hao ◽  
...  
Keyword(s):  
Plant Growth ◽  
Leaf Area ◽  
Plant Height ◽  
High Throughput ◽  
Point Cloud ◽  
Point Clouds ◽  
Morphological Properties ◽  
Cloud Data ◽  
Model Free ◽  
3D Point Clouds

Plant height and leaf area are important morphological properties of leafy vegetable seedlings, and they can be particularly useful for plant growth and health research. The traditional measurement scheme is time-consuming and not suitable for continuously monitoring plant growth and health. Individual vegetable seedling quick segmentation is the prerequisite for high-throughput seedling phenotype data extraction at individual seedling level. This paper proposes an efficient learning- and model-free 3D point cloud data processing pipeline to measure the plant height and leaf area of every single seedling in a plug tray. The 3D point clouds are obtained by a low-cost red–green–blue (RGB)-Depth (RGB-D) camera. Firstly, noise reduction is performed on the original point clouds through the processing of useable-area filter, depth cut-off filter, and neighbor count filter. Secondly, the surface feature histograms-based approach is used to automatically remove the complicated natural background. Then, the Voxel Cloud Connectivity Segmentation (VCCS) and Locally Convex Connected Patches (LCCP) algorithms are employed for individual vegetable seedling partition. Finally, the height and projected leaf area of respective seedlings are calculated based on segmented point clouds and validation is carried out. Critically, we also demonstrate the robustness of our method for different growth conditions and species. The experimental results show that the proposed method could be used to quickly calculate the morphological parameters of each seedling and it is practical to use this approach for high-throughput seedling phenotyping.

scholarly journals Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies

Plant Methods ◽  
2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Xu Wang ◽  
Daljit Singh ◽  
Sandeep Marla ◽  
Geoffrey Morris ◽  
Jesse Poland
Keyword(s):  
Plant Height ◽  
High Throughput ◽  
High Throughput Phenotyping

scholarly journals Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program

2019 ◽  
Vol 11 (21) ◽  
pp. 2494 ◽  
Author(s):  
Alem Gebremedhin ◽  
Pieter Badenhorst ◽  
Junping Wang ◽  
Khageswor Giri ◽  
German Spangenberg ◽  
...  
Keyword(s):  
Plant Height ◽  
High Throughput ◽  
Perennial Ryegrass ◽  
Multispectral Imaging ◽  
Breeding Program ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Sensor Calibration ◽  
Herbage Yield ◽  
High Throughput Phenotyping

Sensor-based phenotyping technologies may offer a non-destructive, high-throughput and efficient assessment of herbage yield (HY) to replace current inefficient phenotyping methods. This paper assesses the feasibility of combining normalised difference vegetative index (NDVI) from multispectral imaging and ultrasonic sonar estimates of plant height to estimate HY of single plants in a large perennial ryegrass breeding program. For sensor calibration, fresh HY (FHY) and dry HY (DHY) were acquired destructively, and plant height was measured at four dates each in 2017 and 2018 from a selected subset of 480 plants. Global multiple linear regression models based on K-fold and random split cross-validation methods were used to evaluate the relationship between observed vs. predicted HY. The coefficient of determination (R2) = 0.67–0.68 and a root mean square error (RMSE) between 5.43–7.60 g was obtained for the validation of predicted vs. observed DHY. The mean absolute error (MAE) and mean percentage error (MPE) ranged between 3.59–5.44 g and 22–28%, respectively. For the FHY, R2 values ranged from 0.63 to 0.70, with an RMSE between 23.50 and 33 g, MAE between 15.11 and 24.34 g and MPE between ~22% and 31%. Combining NDVI and plant height is a robust method to enable high-throughput phenotyping of herbage yield in perennial ryegrass breeding programs.

Sign in / Sign up

Export Citation Format

Share Document