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 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 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 In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR

2018 ◽  
Vol 9 ◽  
Author(s):  
Shangpeng Sun ◽  
Changying Li ◽  
Andrew H. Paterson ◽  
Yu Jiang ◽  
Rui Xu ◽  
...  
Keyword(s):  
Plant Growth ◽  
Cotton Plant ◽  
High Throughput ◽  
Growth Analysis ◽  
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.

Towards high-throughput phenotyping of complex patterned behaviors: Lessons from mouse self-grooming

2011 ◽  
Author(s):  
E. Kyzar ◽  
S. Gaikwad ◽  
M. Pham ◽  
J. Green ◽  
A. Roth ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Phenotyping
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