scholarly journals Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

2020 ◽  
Vol 12 (3) ◽  
pp. 574 ◽  
Author(s):  
Yuncai Hu ◽  
Samuel Knapp ◽  
Urs Schmidhalter
Keyword(s):  
Grain Yield ◽  
Plant Breeding ◽  
High Throughput ◽  
Complex Traits ◽  
New Technologies ◽  
Early Selection ◽  
Spectral Indices ◽  
Wheat Genotypes ◽  
High Throughput Phenotyping ◽  
Spectral Sensing

Enhancing plant breeding to ensure global food security requires new technologies. For wheat phenotyping, only limited seeds and resources are available in early selection cycles. This forces breeders to use small plots with single or multiple row plots in order to include the maximum number of genotypes/lines for their assessment. High-throughput phenotyping through remote sensing may meet the requirements for the phenotyping of thousands of genotypes grown in small plots in early selection cycles. Therefore, the aim of this study was to compare the performance of an unmanned aerial vehicle (UAV) for assessing the grain yield of wheat genotypes in different row numbers per plot in the early selection cycles with ground-based spectral sensing. A field experiment consisting of 32 wheat genotypes with four plot designs (1, 2, 3, and 12 rows per plot) was conducted. Near infrared (NIR)-based spectral indices showed significant correlations (p < 0.01) with the grain yield at flowering to grain filling, regardless of row numbers, indicating the potential of spectral indices as indirect selection traits for the wheat grain yield. Compared with terrestrial sensing, aerial-based sensing from UAV showed consistently higher levels of association with the grain yield, indicating that an increased precision may be obtained and is expected to increase the efficiency of high-throughput phenotyping in large-scale plant breeding programs. Our results suggest that high-throughput sensing from UAV may become a convenient and efficient tool for breeders to promote a more efficient selection of improved genotypes in early selection cycles. Such new information may support the calibration of genomic information by providing additional information on other complex traits, which can be ascertained by spectral sensing.

scholarly journals Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

2020 ◽  
Vol 12 (6) ◽  
pp. 998 ◽  
Author(s):  
GyuJin Jang ◽  
Jaeyoung Kim ◽  
Ju-Kyung Yu ◽  
Hak-Jin Kim ◽  
Yoonha Kim ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
New Technologies ◽  
Cost Effective ◽  
New Wave ◽  
Breeding Programs ◽  
Modern Agriculture ◽  
Aerial Vehicle ◽  
High Throughput Phenotyping

Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

scholarly journals Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding

2021 ◽  
Vol 13 (14) ◽  
pp. 2670
Author(s):  
Paul Herzig ◽  
Peter Borrmann ◽  
Uwe Knauer ◽  
Hans-Christian Klück ◽  
David Kilias ◽  
...  
Keyword(s):  
Grain Yield ◽  
Plant Breeding ◽  
High Throughput ◽  
Prediction Accuracy ◽  
Vegetation Period ◽  
Plant Phenotyping ◽  
Yield Prediction ◽  
Sensor Systems ◽  
Barley Breeding ◽  
High Throughput Phenotyping

With advances in plant genomics, plant phenotyping has become a new bottleneck in plant breeding and the need for reliable high-throughput plant phenotyping techniques has emerged. In the face of future climatic challenges, it does not seem appropriate to continue to solely select for grain yield and a few agronomically important traits. Therefore, new sensor-based high-throughput phenotyping has been increasingly used in plant breeding research, with the potential to provide non-destructive, objective and continuous plant characterization that reveals the formation of the final grain yield and provides insights into the physiology of the plant during the growth phase. In this context, we present the comparison of two sensor systems, Red-Green-Blue (RGB) and multispectral cameras, attached to unmanned aerial vehicles (UAV), and investigate their suitability for yield prediction using different modelling approaches in a segregating barley introgression population at three environments with weekly data collection during the entire vegetation period. In addition to vegetation indices, morphological traits such as canopy height, vegetation cover and growth dynamics traits were used for yield prediction. Repeatability analyses and genotype association studies of sensor-based traits were compared with reference values from ground-based phenotyping to test the use of conventional and new traits for barley breeding. The relative height estimation of the canopy by UAV achieved high precision (up to r = 0.93) and repeatability (up to R2 = 0.98). In addition, we found a great overlap of detected significant genotypes between the reference heights and sensor-based heights. The yield prediction accuracy of both sensor systems was at the same level and reached a maximum prediction accuracy of r2 = 0.82 with a continuous increase in precision throughout the entire vegetation period. Due to the lower costs and the consumer-friendly handling of image acquisition and processing, the RGB imagery seems to be more suitable for yield prediction in this study.

High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage

2019 ◽  
Vol 132 (6) ◽  
pp. 1705-1720 ◽  
Author(s):  
Jin Sun ◽  
Jesse A. Poland ◽  
Suchismita Mondal ◽  
José Crossa ◽  
Philomin Juliana ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genomic Selection ◽  
High Throughput ◽  
Early Stage ◽  
Wheat Grain ◽  
High Throughput Phenotyping ◽  
Selection For

Development of an Automated High- Throughput Phenotyping System for Wheat Evaluation in a Controlled Environment

2019 ◽  
Vol 62 (1) ◽  
pp. 61-74 ◽  
Author(s):  
Chongyuan Zhang ◽  
Chongyuan Zhang ◽  
Michael O. Pumphrey ◽  
Jianfeng Zhou ◽  
Qin Zhang ◽  
...  
Keyword(s):  
Image Processing ◽  
Heat Stress ◽  
Plant Breeding ◽  
Stripe Rust ◽  
High Throughput ◽  
Controlled Environment ◽  
High Throughput Phenotyping ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Wheat Lines

Abstract. Plant breeding has significantly improved in recent years; however, phenotyping remains a bottleneck, as the process of evaluating and measuring plant traits is often expensive, subjective, and laborious. Although commercial phenotyping systems are available, factors like cost, space, and need for specific controlled-environment conditions limit the affordability of these products. An accurate, user-friendly, adaptive, and high-throughput phenotyping (HTP) system is highly desirable to plant breeders, physiologists, and agronomists. To solve this problem, an automated HTP system and image processing algorithms were developed and tested in this study. The automated platform was an integration of an aluminum framework (including movement and control components), three cameras, and a laptop computer. A control program was developed using LabVIEW to manage operation of the system frame and sensors as a single-unit automated HTP system. Image processing algorithms were developed in MATLAB for high-throughput analysis of images acquired by the system to estimate phenotypes and traits associated with tested plants. The phenotypes extracted were color/spectral, texture, temperature, morphology, and greenness features on a temporal scale. Using two wheat lines with known heat tolerance, the functions of the HTP system were validated. Heat stress tolerance experiments revealed that features such as green leaf area and green normalized difference vegetation index derived from our system showed differences between the control and heat stress treatments, as well as between heat-tolerant and susceptible wheat lines. In another experiment, stripe rust resistance in wheat was assessed. With the HTP system, some potential for detecting qualitative traits, such as disease resistance, was observed, although further validation is needed. In summary, successful development and implementation of an automated system with custom image processing algorithms for HTP in wheat was achieved. Improvement of such systems would further help plant breeders, physiologists, and agronomists to phenotype crops in an efficient, objective, and high-throughput manner. Keywords: Automation, Heat stress, Image processing, Plant breeding, Sensing, Stripe rust.

Identification of stay-green and early senescence phenotypes in high-yielding winter wheat, and their relationship to grain yield and grain protein concentration using high-throughput phenotyping techniques

2014 ◽  
Vol 41 (3) ◽  
pp. 227 ◽  
Author(s):  
Sebastian Kipp ◽  
Bodo Mistele ◽  
Urs Schmidhalter
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
High Throughput ◽  
Protein Concentration ◽  
Partial Least Square ◽  
Large Field ◽  
Grain Protein ◽  
Stay Green ◽  
Grain Protein Concentration ◽  
High Throughput Phenotyping

Yield and grain protein concentration (GPC) represent crucial factors in the global agricultural wheat (Triticum aestivum L.) production and are predominantly determined via carbon and nitrogen metabolism, respectively. The maintenance of green leaf area and the onset of senescence (Osen) are expected to be involved in both C and N accumulation and their translocation into grains. The aim of this study was to identify stay-green and early senescence phenotypes in a field experiment of 50 certified winter wheat cultivars and to investigate the relationships among Osen, yield and GPC. Colour measurements on flag leaves were conducted to determine Osen for 20 cultivars and partial least square regression models were used to calculate Osen for the remaining 30 cultivars based on passive spectral reflectance measurements as a high-throughput phenotyping technique for all varieties. Using this method, stay-green and early senescence phenotypes could be clearly differentiated. A significant negative relationship between Osen and grain yield (r2 = 0.81) was observed. By contrast, GPC showed a significant positive relationship to Osen (r2 = 0.48). In conclusion, the high-throughput character of our proposed phenotyping method should help improve the detection of such traits in large field trials as well as help us reach a better understanding of the consequences of the timing of senescence on yield.

scholarly journals Aerial High-Throughput Phenotyping Enabling Indirect Selection for Grain Yield at the Early-generation Seed-limited Stages in Breeding Programs

2020 ◽  
Author(s):  
Margaret R. Krause ◽  
Suchismita Mondal ◽  
José Crossa ◽  
Ravi P. Singh ◽  
Francisco Pinto ◽  
...  
Keyword(s):  
Grain Yield ◽  
High Throughput ◽  
Vegetation Indices ◽  
Early Generation ◽  
Breeding Programs ◽  
Breeding Lines ◽  
High Throughput Phenotyping ◽  
Seed Increase ◽  
Yield Trials ◽  
Selection Of

ABSTRACTBreeding programs for wheat and many other crops require one or more generations of seed increase before replicated yield trials can be sown. Extensive phenotyping at this stage of the breeding cycle is challenging due to the small plot size and large number of lines under evaluation. Therefore, breeders typically rely on visual selection of small, unreplicated seed increase plots for the promotion of breeding lines to replicated yield trials. With the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield. We evaluated early generation material in the irrigated bread wheat (Triticum aestivum L.) breeding program at the International Maize and Wheat Improvement Center to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield. To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles. Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and moderately correlated with grain yield assessed in replicated yield trials. Furthermore, vegetation indices were more predictive of grain yield than univariate genomic selection, while multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall. A related experiment showed that selection approaches for grain yield based on vegetation indices could be more effective than visual selection; however, selection on the vegetation indices alone would have also driven a directional response in phenology due to confounding between those traits. A restricted selection index was proposed for improving grain yield without affecting the distribution of phenology in the breeding population. The results of these experiments provide a promising outlook for the use of aerial high-throughput phenotyping traits to improve selection at the early-generation seed-limited stage of wheat breeding programs.

scholarly journals Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

2020 ◽  
Vol 11 ◽  
Author(s):  
Tiago Bresolin ◽  
João R. R. Dórea
Keyword(s):  
Electromagnetic Radiation ◽  
High Throughput ◽  
Complex Traits ◽  
Infrared Spectrometry ◽  
Electromagnetic Spectrum ◽  
Wide Range ◽  
Level Information ◽  
High Throughput Phenotyping ◽  
The Impact ◽  
Livestock Systems

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.

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