scholarly journals Development and Application of Image-Based High-Throughput Phenotyping Methodology for Salt Tolerance in Lentils

Agronomy ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1992
Author(s):  
Ruwani Dissanayake ◽  
Hossein V. Kahrood ◽  
Adam M. Dimech ◽  
Dianne M. Noy ◽  
Garry M. Rosewarne ◽  
...  
Keyword(s):  
Salt Tolerance ◽  
High Throughput ◽  
Phenotypic Trait ◽  
Screening Methods ◽  
Toxicity Screening ◽  
Salt Level ◽  
Optimized Protocol ◽  
High Throughput Phenotyping ◽  
Modern Breeding ◽  
Salt Toxicity

Soil salinity is a major abiotic stress in Australian lentil-producing areas. It is therefore imperative to identify genetic variation for salt tolerance in order to develop lentil varieties suitable for saline soils. Conventional screening methods include the manual assessment of stress symptoms, which can be very laborious, time-consuming, and error-prone. Recent advances in image-based high-throughput phenotyping (HTP) technologies have provided unparalleled opportunities to screen plants for a range of stresses, such as salt toxicity. The current study describes the development and application of an HTP method for salt toxicity screening in lentils. In a pilot study, six lentil genotypes were evaluated to determine the optimal salt level and the growth stage for distinguishing lentil genotypes using red–green–blue (RGB) images on a LemnaTec Scanalyzer 3D phenomics platform. The optimized protocol was then applied to screen 276 accessions that were also assessed earlier in a conventional phenotypic screen. Detailed phenotypic trait assessments, including plant growth and green/non-green color pixels, were made and correlated to the conventional screen (r = 0.55; p < 0.0001). These findings demonstrated the improved efficacy of an image-based phenotyping approach that is high-throughput, efficient, and better suited to modern breeding programs.

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.

scholarly journals High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254908
Author(s):  
Sameer Joshi ◽  
Emily Thoday-Kennedy ◽  
Hans D. Daetwyler ◽  
Matthew Hayden ◽  
German Spangenberg ◽  
...  
Keyword(s):  
Drought Stress ◽  
Drought Tolerance ◽  
High Throughput ◽  
Crop Yields ◽  
Geometric Mean ◽  
Shoot Biomass ◽  
Screening Methods ◽  
Tolerance Index ◽  
Genotypic Differences ◽  
High Throughput Phenotyping

Drought is one of the most severe and unpredictable abiotic stresses, occurring at any growth stage and affecting crop yields worldwide. Therefore, it is essential to develop drought tolerant varieties to ensure sustainable crop production in an ever-changing climate. High-throughput digital phenotyping technologies in tandem with robust screening methods enable precise and faster selection of genotypes for breeding. To investigate the use of digital imaging to reliably phenotype for drought tolerance, a genetically diverse safflower population was screened under different drought stresses at Agriculture Victoria’s high-throughput, automated phenotyping platform, Plant Phenomics Victoria, Horsham. In the first experiment, four treatments, control (90% field capacity; FC), 40% FC at initial branching, 40% FC at flowering and 50% FC at initial branching and flowering, were applied to assess the performance of four safflower genotypes. Based on these results, drought stress using 50% FC at initial branching and flowering stages was chosen to further screen 200 diverse safflower genotypes. Measured plant traits and dry biomass showed high correlations with derived digital traits including estimated shoot biomass, convex hull area, caliper length and minimum area rectangle, indicating the viability of using digital traits as proxy measures for plant growth. Estimated shoot biomass showed close association having moderately high correlation with drought indices yield index, stress tolerance index, geometric mean productivity, and mean productivity. Diverse genotypes were classified into four clusters of drought tolerance based on their performance (seed yield and digitally estimated shoot biomass) under stress. Overall, results show that rapid and precise image-based, high-throughput phenotyping in controlled environments can be used to effectively differentiate response to drought stress in a large numbers of safflower genotypes.

scholarly journals A UAV-based high-throughput phenotyping approach to assess time-series nitrogen responses and identify traits associated genetic components in maize

2021 ◽  
Author(s):  
Jinliang Yang ◽  
Eric Rodene ◽  
Gen Xu ◽  
Christine Smith ◽  
Yufeng Ge ◽  
...  
Keyword(s):  
Time Series ◽  
High Throughput ◽  
Pearson Correlation ◽  
Phenotypic Trait ◽  
Phenotypic Traits ◽  
Time Points ◽  
Canopy Coverage ◽  
Genetic Components ◽  
Genome Wide ◽  
High Throughput Phenotyping

Advancements in the use of genome-wide markers have provided new opportunities for dissecting the genetic components that control phenotypic trait variation. However, cost-effectively characterizing agronomically important phenotypic traits on a large scale remains a bottleneck. Unmanned aerial vehicle (UAV)-based high-throughput phenotyping has recently become a prominent method, as it allows large numbers of plants to be analyzed in a time-series manner. In this experiment, 233 inbred lines from the maize diversity panel were grown in a replicated incomplete block under both nitrogen-limited conditions and following conventional agronomic practices. UAV images were collected during different plant developmental stages throughout the growing season. A pipeline for extracting plot-level images, filtering images to remove non-foliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed. After applying the pipeline, about half a million plot-level image clips were obtained for 12 different time points. High correlations were detected between VIs and ground truth physiological and yield-related traits collected from the same plots, i.e., Vegetative Index (VEG) vs. leaf nitrogen levels (Pearson correlation coefficient, R = 0.73), Woebbecke index vs. leaf area (R = -0.52), and Visible Atmospherically Resistant Index (VARI) vs. 20 kernel weight --- a yield component trait (R = 0.40). The genome-wide association study was performed using canopy coverage and each of the VIs at each date, resulting in N = 29 unique genomic regions associated with image extracted traits from three or more of the 12 total time points. A candidate gene Zm00001d031997, a maize homolog of the Arabidopsis HCF244 (high chlorophyll fluorescence 244), located underneath the leading SNPs of the canopy coverage associated signals that were repeatedly detected under both nitrogen conditions. The plot-level time-series phenotypic data and the trait-associated genes provide great opportunities to advance plant science and to facilitate plant breeding.

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

From Classical to High Throughput Screening Methods for Feruloyl Esterases: A Review

2016 ◽  
Vol 19 (8) ◽  
pp. 616-626 ◽  
Author(s):  
Lorena Ramírez-Velasco ◽  
Mariana Armendáriz-Ruiz ◽  
Jorge Alberto Rodríguez-González ◽  
Marcelo Müller-Santos ◽  
Ali Asaff-Torres ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Screening Methods ◽  
Feruloyl Esterases

scholarly journals High-throughput phenotyping: breaking through the bottleneck in future crop breeding

2021 ◽  
Author(s):  
Peng Song ◽  
Jinglu Wang ◽  
Xinyu Guo ◽  
Wanneng Yang ◽  
Chunjiang Zhao
Keyword(s):  
High Throughput ◽  
Crop Breeding ◽  
High Throughput Phenotyping

scholarly journals High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops

2021 ◽  
Vol 22 (15) ◽  
pp. 8266
Author(s):  
Minsu Kim ◽  
Chaewon Lee ◽  
Subin Hong ◽  
Song Lim Kim ◽  
Jeong-Ho Baek ◽  
...  
Keyword(s):  
Drought Stress ◽  
High Throughput ◽  
Large Scale ◽  
Crop Yields ◽  
Plant Traits ◽  
Rapid Development ◽  
Plant Responses ◽  
Phenotypic Data ◽  
Agricultural Technologies ◽  
High Throughput Phenotyping

Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.

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