Browsing by Author "Oakes, Joseph"

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    Effect of Year, Location, Cropping System, Maturity Group, and Variety on Protein and Oil Content of Virginia Soybean 2019-2020
    Bond, Kayla; Holshouser, David L.; Zhang, Bo; Oakes, Joseph (Virginia Tech, 2021-12-13)
    Soybean is a crop valued highly for its protein and oil content. Although soybean varieties have been regularly tested in Virginia for yield and other performance parameters, protein and oil measurements have not been collected prior to 2019. The purpose of this project was to compile data to determine if protein and oil content of Virginia soybean varied between year, location, cropping system, maturity group, or variety. Data was collected across five locations in Virginia over the 2019 and 2020 growing seasons for maturity groups 3, 4, and 5 in full-season and double-crop systems. While all factors examined affected protein and oil content, there was no consistency across time, location, cropping system, or maturity group. All factors interacted with each other; 2-, 3-, and 4-way interactions were present. There were differences in varieties for all experiments, except there were no protein differences for maturity group 3 varieties in either year; and, there were no oil differences in maturity group 3 varieties in 2019. Although cropping system differences occurred in 6 of 8 year-maturity group combinations and in 7 of 8 year-maturity group combinations for oil, in only one instance did cropping system interact with variety, indicating that differences of protein and oil content of varieties were relatively stable over cropping systems. Further data collection and analysis is necessary to determine consistencies within variables that affect protein content or oil content of Virginia soybean.
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    Evaluation of the U.S. Peanut Germplasm Mini-Core Collection in the Virginia-Carolina Region Using Traditional and New High-Throughput Methods
    Sarkar, Sayantan; Oakes, Joseph; Cazenave, Alexandre-Brice; Burow, Mark D.; Bennett, Rebecca S.; Chamberlin, Kelly D.; Wang, Ning; White, Melanie; Payton, Paxton; Mahan, James; Chagoya, Jennifer; Sung, Cheng-Jung; McCall, David S.; Thomason, Wade E.; Balota, Maria (MDPI, 2022-08-18)
    Peanut (Arachis hypogaea L.) is an important food crop for the U.S. and the world. The Virginia-Carolina (VC) region (Virginia, North Carolina, and South Carolina) is an important peanut-growing region of the U.S and is affected by numerous biotic and abiotic stresses. Identification of stress-resistant germplasm, along with improved phenotyping methods, are important steps toward developing improved cultivars. Our objective in 2017 and 2018 was to assess the U.S. mini-core collection for desirable traits, a valuable source for resistant germplasm under limited water conditions. Accessions were evaluated using traditional and high-throughput phenotyping (HTP) techniques, and the suitability of HTP methods as indirect selection tools was assessed. Traditional phenotyping methods included stand count, plant height, lateral branch growth, normalized difference vegetation index (NDVI), canopy temperature depression (CTD), leaf wilting, fungal and viral disease, thrips rating, post-digging in-shell sprouting, and pod yield. The HTP method included 48 aerial vegetation indices (VIs), which were derived using red, blue, green, and near-infrared reflectance; color space indices were collected using an octocopter drone at the same time, with traditional phenotyping. Both phenotypings were done 10 times between 4 and 16 weeks after planting. Accessions had yields comparable to high yielding checks. Correlation coefficients up to 0.8 were identified for several Vis, with yield indicating their suitability for indirect phenotyping. Broad-sense heritability (H2) was further calculated to assess the suitability of particular VIs to enable genetic gains. VIs could be used successfully as surrogates for the physiological and agronomic trait selection in peanuts. Further, this study indicates that UAV-based sensors have potential for measuring physiologic and agronomic characteristics measured for peanut breeding, variable rate input application, real time decision making, and precision agriculture applications.
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    Integrating Cybersecurity and Agricultural Innovation
    Drape, Tiffany A.; Thompson, Cris; Johnson, Kellie; Brown, Anne M.; Simpson, Joseph; Oakes, Joseph; Duncan, Sue; Westfall-Rudd, Donna M. (Virginia Tech, 2022-08-10)
    This 1-2 credit undergraduate course, as presented, is designed to provide an interdisciplinary, experiential-learning-based background and exposure to working on and completing a team project in cyberbiosecurity in agriculture and the life sciences. These modules and capstone are designed for students to learn about cyberbiosecurity and how their agriculture knowledge can provide employment opportunities related to cyberbiosecurity. This course will provide knowledge and training on cyberbiosecurity, issues with online data and security, how we might protect our biological data, and ethical implications of biological data sharing and ownership. The course will teach critical thinking and problem-solving in a team environment, professional presentations, and writing skills in the context of completing the capstone project.
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    Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels
    Dhakal, Kshitiz; Sivaramakrishnan, Upasana; Zhang, Xuemei; Belay, Kassaye; Oakes, Joseph; Wei, Xing; Li, Song (MDPI, 2023-03-28)
    Fusarium head blight (FHB) is a disease of small grains caused by the fungus Fusarium graminearum. In this study, we explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels. We evaluated the use of HSI for disease classification and correlated the damage with the mycotoxin deoxynivalenol (DON) content. Computational analyses were carried out to determine which machine learning methods had the best accuracy to classify different levels of damage in wheat kernel samples. The classes of samples were based on the DON content obtained from Gas Chromatography–Mass Spectrometry (GC-MS). We found that G-Boost, an ensemble method, showed the best performance with 97% accuracy in classifying wheat kernels into different severity levels. Mask R-CNN, an instance segmentation method, was used to segment the wheat kernels from HSI data. The regions of interest (ROIs) obtained from Mask R-CNN achieved a high mAP of 0.97. The results from Mask R-CNN, when combined with the classification method, were able to correlate HSI data with the DON concentration in small grains with an R2 of 0.75. Our results show the potential of HSI to quantify DON in wheat kernels in commercial settings such as elevators or mills.
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    Specialty Small Grains in 2020
    Thomason, Wade E.; Griffey, Carl A.; Mehl, Hillary; Lawton, Nathaniel A.; Rucker, Elizabeth; Brooks, Wynse S.; Liu, Limei; Custis,nTo; Langston, David B.; Byrd-Masters, Linda; Byum, Steve; Kaur, Navjot; Oakes, Joseph; Vaughn, Mark; Jones, Ned; Light, Jon; Clark, Bobby; Lillar, Gregory (Virginia Cooperative Extension, 2020-12-17)
    This publication provides results from specialty wheat and barley varietal tests conducted in Virginia in 2018-2020. The tests provide information to assist Virginia Cooperative Extension Service agents in formulating cultivar recommendations for small grain producers and to companies developing cultivars and/or marketing seed within the state.