We recently applied for a phenomic infrastructure grant to Department of Defence to obtain high throughput phenotyper. We will continue to incorporate phenomic modules for teaching and research endeavors in crop breeding. Using this teaching capacity building grant, we developed various modules for phenomics using high throughput techniques, field level plant breeding and metabolite quantification for enhancing the crop improvement curriculum at West Virginia State University.
In addition to genome assisted plant breeding, we developed several modules in function genomics to be used various courses. We conducted hands on workshops for WVSU and other land-grant universities in the areas of plant breeding and genomics. BMC Plant Biology 16 1 Food Chemistry Sponsoring Institution.
Project Director Reddy, U. Non Technical Summary Plant phenotyping refers to high-throughput characterization of plant traits in a quantitative description for use in GWAS combined with high-throughput single nucleotide polymorphism SNP resources. Phenotyping refers to the holistic characterization of plant performance by multispectral image analyses via thermographic analyses of shoot and root growth with non-destructive, image-analysis-based phenotyping under controlled conditions as well as in field plots and canopies.
Once our students have been exposed to modern phenotyping and GWAS techniques, they will be able to compete for jobs or pursue advanced or additional education in one of the fastest-growing areas of research and industry. We will create a plethora of opportunities for students to pursue research in phenomics and modern methods in plant breeding and will expose them to the concept of plant breeding for food security.
Animal Health Component.
Translating High-Throughput Phenotyping into Genetic Gain - Europe PMC Article - Europe PMC
Research Effort Categories Basic. Our goal is to make it much simpler for our high school teachers to provide their students with the laboratory experiences that mirror the open-ended explorations of scientists, instead of the traditional "cookbook" labs where the students follow instructions to a predetermined result. We aim to develop lab classes that involve plant biodiversity, impact of natural products in human health in addition to the core biotechnology modules for the biology courses taught at the advanced high-school level.
We plan to aim to expose students in elementary, middle and high schools with many hours of high-quality science inquiry, supported by carefully prepared science specialists. In this way, "science education" would be redefined, with a laser-sharp focus on gaining the scientific habits of mind that will be needed by everyone to successfully negotiate his or her way through our increasingly complex and confusing science higher education.
To this end, we will also organize science contests that involve successful student independent experimentation. These contests will be followed by a social gathering of distinguished visitor, WVSU researchers and the students that would generate new research and collaborative opportunities at WVSU and the students will develop interests in the various fields of biology further to explore study opportunities at WVSU.
The overall "program is conceived to bring together and strengthen existing expertise in phenome analyses and infuse modern phenomic technologies into a workshop that will establish a US food safety focus at WVSU. The specific objectives are described below. Objective 1: Organize special summer workshops for faculty, graduate students and undergraduate levels for extensive training in phenomics and GWAS pipelines. Objective 2: Monthly seminars by distinguished scientists from various fields in phenomics and post-seminar get-togethers.
The software automatically determines the boundaries of a fruit in a scanned image via contour tracing, which results in a list of adjacent points describing the border of an object in an image.
All fruit shape measurements are based on boundaries. The color test module "Color Test" is designed to quantify the color parameters inside the recognized boundaries. A step-by-step protocol is described for digitalization of fruits and subsequent semi-automatic analysis of morphology and color attributes with the TA software package. The protocol is divided into 8 steps: 1 image collection, 2 image analysis and calibration for the Color Test, 3 manual adjustment of attributes, 4 user-defined settings, 5 saving and exporting data, and 6 examples of data analysis.
Length of trichomes is measured by using ImageJ Schneider et al.
Applications of Phenotyping Methods for Crop Improvement
Step 1. The option "line" is chosen to create straight, segmented or freehand lines along the trichomes. Information about the line is displayed on the status bar. Step 2. The ruler option is set over the trichomes. In the Set Scale option, the length of the line is shown in pixels.
Calibrate with the known length and select units of measurement in the appropriate boxes in our case 5 mm and click OK. This data was analysed with mixed linear models in the freely available statistical software R R-Core Team This phenotyping boom is mounted on a tractor and houses four digital cameras and a LiDAR unit, all of which are triggered via an RTK GPS, capturing data for individual plots within field trials. This phenotyping boom was used towards the end of the growing season in trials designed to maximise phenotypic variation six phenotypically contrasting varieties, with four sowing rates.
Digital photos were taken for continued algorithm development and LiDAR scans were captured for height and biomass estimation.
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Manual plant heights and biomass samples were collected from each plot in the trial. This data is being used, in conjunction with the LiDAR data, in the ongoing development of algorithms for the estimation of plant height and biomass. Digital scores for physiological yellowing percentage leaf area yellow , processed in Image J Schneider et al.
Heritability a measure of accuracy used in plant breeding was 0. Figure 1.
The percentage of leaf area yellow within wheat plots, scored visually and digitally with Image J. Processing of images to measure physiological yellowing in Image J was a simple process Figure 2 , following similar methods of colour segmentation used by Adamsen et al. Figure 2. The three levels of Image segmentation using Image J. With Image J being freely available and the relatively simple process required for image analysis, this technique can be readily used by anyone.
Potential exists for these methods to be used in the measurement of other traits such as ground cover, early vigour, senescence and perhaps disease.
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Use of these methods within large scale plant breeding trials will be conducted in Data collected from the LiDAR unit can be processed to determine crop canopy height, with this showing a strong correlation to manually-measured canopy heights Figure 3. LiDAR measurements were consistently lower than manual measurements, with this likely being a result of the way in which the algorithm calculates height from the LiDAR data.
Heritability calculated from both the manual and LiDAR measurements showed the same result of 0. Figure 3. Canopy height of wheat plots as measured manually and with a LiDAR sensor. The application of this data to statistical analyses within a wheat breeding program is still being investigated, however this data may be useful for breeding other crop species or for agronomic research. LiDAR data is also being investigated for its potential to measure crop biomass, for use in multivariate statistical analyses. So far during this project, we have collected data from wheat plots using digital cameras and a LiDAR sensor, further extracting information from these relating to potential traits of interest for wheat breeders.
In the case of digital images image capture and data extraction was a relatively simple process, conducted using open source software. These techniques can be used by anyone with a digital camera, simply by photographing plots as you walk along and then post processing the photos in Image J. LiDAR, on the other hand, is much more difficult to work with. Proprietary software is required to interface with the LiDAR itself, allowing for raw data collection, with this data then needing further processing to make it of any value to researchers.
This latter level of processing is achieved with custom coding and scripting in this case using R and requires a specialised skill set. Use of these technologies for agricultural research in particular LiDAR is in the very early stages of development, and so it is unsurprising that there are some difficulties applying them to field research.
Translating High-Throughput Phenotyping into Genetic Gain
However, as more research is conducted, it is likely that easy to use tools will be developed, allowing a wider audience of researchers to take advantage of these sensors and the data they produce. This project will continue in with large scale data capture of digital images and LiDAR data from field trials. Different analysis techniques of this data will be investigated, as well as the different applications this data may have within a wheat breeding program.
Measuring Wheat Senescence with a Digital Camera. Crop Science 39, Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments. Annals of Applied Biology ,
Related Phenotyping for Plant Breeding: Applications of Phenotyping Methods for Crop Improvement
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