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.

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 High-throughput volumetric reconstruction for 3D wheat plant architecture studies

2016 ◽  
Vol 09 (05) ◽  
pp. 1650037 ◽  
Author(s):  
Wei Fang ◽  
Hui Feng ◽  
Wanneng Yang ◽  
Lingfeng Duan ◽  
Guoxing Chen ◽  
...  
Keyword(s):  
Plant Height ◽  
High Throughput ◽  
Plant Architecture ◽  
Three Dimensional ◽  
Wheat Plant ◽  
Percentage Error ◽  
Processing Unit ◽  
Fresh Weight ◽  
Volumetric Reconstruction ◽  
Average Plant

For many tiller crops, the plant architecture (PA), including the plant fresh weight, plant height, number of tillers, tiller angle and stem diameter, significantly affects the grain yield. In this study, we propose a method based on volumetric reconstruction for high-throughput three-dimensional (3D) wheat PA studies. The proposed methodology involves plant volumetric reconstruction from multiple images, plant model processing and phenotypic parameter estimation and analysis. This study was performed on 80 Triticum aestivum plants, and the results were analyzed. Comparing the automated measurements with manual measurements, the mean absolute percentage error (MAPE) in the plant height and the plant fresh weight was 2.71% (1.08[Formula: see text]cm with an average plant height of 40.07[Formula: see text]cm) and 10.06% (1.41[Formula: see text]g with an average plant fresh weight of 14.06[Formula: see text]g), respectively. The root mean square error (RMSE) was 1.37[Formula: see text]cm and 1.79[Formula: see text]g for the plant height and plant fresh weight, respectively. The correlation coefficients were 0.95 and 0.96 for the plant height and plant fresh weight, respectively. Additionally, the proposed methodology, including plant reconstruction, model processing and trait extraction, required only approximately 20[Formula: see text]s on average per plant using parallel computing on a graphics processing unit (GPU), demonstrating that the methodology would be valuable for a high-throughput phenotyping platform.

Evaluation of herbage yield in a forage grass breeding program: comparison of visual rating versus measurement in single-row plots or swards

2001 ◽  
Vol 41 (8) ◽  
pp. 1161 ◽  
Author(s):  
K. F. Smith ◽  
M. Tasneem ◽  
G. A. Kearney ◽  
K. F. M. Reed ◽  
A. Leonforte
Keyword(s):  
Correlation Coefficient ◽  
Perennial Ryegrass ◽  
Dry Matter ◽  
Rank Correlation ◽  
Breeding Program ◽  
Yield Potential ◽  
Dry Matter Yield ◽  
Single Row ◽  
Visual Rating ◽  
Herbage Yield

To refine selection methods for a perennial ryegrass (Lolium perenne L.) breeding program, half-sib families and commercial cultivars were evaluated for 3 years with treatments sown as both single-drill rows or swards. Dry matter yield of the perennial ryegrass treatments was evaluated several times in each year as a visual score which was subsequently calibrated against a regression determined by cutting a subset of plots or by cutting all plots. Thus, the experiment evaluated 2 aspects of herbage-yield determination in a perennial ryegrass breeding program: (i) the use of visual estimates of herbage yield to reduce the time spent cutting plots, and (ii) the use of single-row plots compared with swards. The correlation (either as Pearsons correlation coefficient, or Spearmans rank correlation coefficient) between visual estimates of herbage yield was always significant (P<0.01), with the exception of the rank correlation for sward plots in the summer 1995 (r = 0.4; P<0.05). However, the extent of the correlation varied (r = 0.4–0.9), and at some harvests calibrated visual ratings only explained a small proportion of the variance observed in harvested dry matter yields. These data suggest that visual ratings of herbage yield would be accurate enough to be used to detect large differences between families, breeding lines, cultivars or accessions of perennial ryegrass. However, when differences between lines are likely to be small, then harvesting all plots would give a more accurate estimate of the yield of perennial ryegrass lines. Likewise, the herbage yield of perennial ryegrass in single-row plots was significantly correlated with the herbage yield of perennial ryegrass sown as swards (P<0.01 or P<0.05). However, the correlation was again variable leading to the conclusion that evaluation of perennial ryegrass as single-row plots was not always an accurate indicator of sward yield. For those 4 (of 13) harvests over 3 years where the interaction between sward yield and row yield of the perennial ryegrass lines was significant (P<0.05), this interaction was shown not to be due to significant rank changes but rather to an increase in the differences of yield in swards or yield in single-row plots. We conclude that the harvesting of swards was the most reliable method of estimating the dry matter yield of perennial ryegrass cultivars. However, significant correlations between visual rating of treatments, or yield in single-row plots and measured yield as swards illustrated that these methods (visual ratings and single-plot yields) could be used to reduce the cost of evaluating differences in the herbage yield potential of perennial ryegrass, especially when these differences were likely to be large or when seed is limited, such as during the evaluation of accessions.

scholarly journals UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence

2019 ◽  
Vol 11 (4) ◽  
pp. 410 ◽  
Author(s):  
Yiannis Ampatzidis ◽  
Victor Partel
Keyword(s):  
Artificial Intelligence ◽  
Data Acquisition ◽  
High Throughput ◽  
Multispectral Imaging ◽  
Phenotypic Trait ◽  
Phenotypic Characteristics ◽  
Individual Tree ◽  
High Throughput Phenotyping ◽  
Citrus Varieties ◽  
Canopy Size

Traditional plant breeding evaluation methods are time-consuming, labor-intensive, and costly. Accurate and rapid phenotypic trait data acquisition and analysis can improve genomic selection and accelerate cultivar development. In this work, a technique for data acquisition and image processing was developed utilizing small unmanned aerial vehicles (UAVs), multispectral imaging, and deep learning convolutional neural networks to evaluate phenotypic characteristics on citrus crops. This low-cost and automated high-throughput phenotyping technique utilizes artificial intelligence (AI) and machine learning (ML) to: (i) detect, count, and geolocate trees and tree gaps; (ii) categorize trees based on their canopy size; (iii) develop individual tree health indices; and (iv) evaluate citrus varieties and rootstocks. The proposed remote sensing technique was able to detect and count citrus trees in a grove of 4,931 trees, with precision and recall of 99.9% and 99.7%, respectively, estimate their canopy size with overall accuracy of 85.5%, and detect, count, and geolocate tree gaps with a precision and recall of 100% and 94.6%, respectively. This UAV-based technique provides a consistent, more direct, cost-effective, and rapid method to evaluate phenotypic characteristics of citrus varieties and rootstocks.

Sign in / Sign up

Export Citation Format

Share Document