,For this study, 737 sweetpotato accessions were obtained from the USDA, ARS, PGRCU, Griffin, GA. Each PI was grown in the field in replicated plots at the U. S. Vegetable Laboratory, Charleston, SC. The mature leaves of each PI was collected and measured using a Konica Minolta Chroma Meter (CR 400). Data were recorded using CIE 1976 Lab and CIE LCh color spaces. Data from this study is contained in a manuscript that will be submitted to Genetic Resources and Crop Evolution under the title "Color Analysis of Sweetpotato Leaves from the USDA, ARS Germplasm Collection." Data parameters collected were lightness (L), red-green coordinate (a), yellow-blue coordinate (b), color intensity or chroma (C), and hue angle (h*).,,

,Sweetpotato (Ipomoea batatas) plays a critical role in food security and is the most important root crop worldwide following potatoes and cassava. In the United States (US), it is valued at over $700 million USD. There are two sweetpotato germplasm collections (Plant Genetic Resources Conservation Unit and US Vegetable Laboratory) maintained by the USDA, ARS for sweetpotato crop improvement. To date, no genome-wide assessment of genetic diversity within these collections has been reported in the published literature. In our study, population structure and genetic diversity of 417 USDA sweetpotato accessions originating from 8 broad geographical regions (Africa, Australia, Caribbean, Central America, Far East, North America, Pacific Islands, and South America) were determined using single nucleotide polymorphisms (SNPs) identified with a genotyping-by-sequencing (GBS) protocol, GBSpoly, optimized for highly heterozygous and polyploid species. Population structure using Bayesian clustering analyses (STRUCTURE) with 32,784 segregating SNPs grouped the accessions into four genetic groups and indicated a high degree of mixed ancestry. A neighbor-joining cladogram and principal components analysis based on a pairwise genetic distance matrix of the accessions supported the population structure analysis. Pairwise FST values between broad geographical regions based on the origin of accessions ranged from 0.017 (Far East – Pacific Islands) to 0.110 (Australia – South America) and supported the clustering of accessions based on genetic distance. The markers developed for use with this collection of accessions provide an important genomic resource for the sweetpotato community, and contribute to our understanding of the genetic diversity present within the US sweetpotato collection and the species.,,

This data release contains reflectance spectra of residue (senesced vegetation) for common row crops (corn, soybean, winter wheat) and cover crops (cereals, legumes, brassicas). Two-hundred and ninety-six cash and cover crop spectra were collected in the laboratory using Analytical Spectral Devices (ASD) spectrophotometers. Sixty-five physical samples were collected in the field that pair with the Italian Space Agency's spaceborne PRecursore IperSpettrale della Missione Applicativa (PRISMA) imaging spectrometer. The data release also contains biochemical trait concentrations (i.e., nitrogen, nonstructural carbohydrates, holocellulose, and lignin) from physical samples used to evaluate biochemical trait mapping of cash and crop cover residue. Data collection occurred at the USDA-ARS Beltsville Agricultural Research Center in Beltsville, MD, USA or on the Eastern Shore of MD, USA between 2010 and 2022. The data, as well as the processes used to prepare and analyze them, are discussed in detail in a related interpretive summary: Jennewein, J.S., W.D. Hively, B.T. Lamb, C.S.T. Daughtry, R. Thapa, A. Thieme, C. Reberg-Horton, and S. Mirsky. 2024. Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues. Precision Agriculture. https:/doi.org/ Contents: 1. Metadata Row crop and cover crop residue spectra from lab spectrometer and spaceborne PRISMA imagery, Maryland, USA.xml : metadata file describing dataset parameters 2. FieldSpec4_ASD_mean_corrected_reflectance_spectra_cash_and_cover_crops.csv : comma delimited spreadsheet containing cash and cover crop biochemical traits with ASD reflectance spectra collected in the lab 3. PRISMA_reflectance_spectra_smoothed_brightness_normalized_cash_and_cover_crops.csv : comma delimited spreadsheet containing sample biochemical traits with PRISMA spaceborne surface reflectance spectra that have been smoothed and brightness normalized associated with field sampling locations Additional works cited in this metadatafile: Berger, K., Hank, T., Halabuk, A., Rivera-Caicedo, J. P., Wocher, M., Mojses, M., Gerhátová, K., Tagliabue, G., Dolz, M. M., Venteo, A. B. P., and Verrelst, J. (2021). Assessing non-photosynthetic cropland biomass from spaceborne hyperspectral imagery. Remote Sensing, 13(22), 1–20. https://doi.org/10.3390/rs13224711 Daughtry, C. S. T., Serbin, G., Iii, J. B. R., Doraiswamy, P. C., Raymond, E., and Jr, H. (2010). Spectral Reflectance of Wheat Residue during Decomposition and Remotely Sensed Estimates of Residue Cover. Remote Sensing, 2(2), 416–431. https://doi.org/10.3390/rs2020416 Feilhauer, H., Asner, G. P., Martin, R. E., and Schmidtlein, S. (2010). Brightness-normalized Partial Least Squares Regression for hyperspectral data. Journal of Quantitative Spectroscopy and Radiative Transfer, 111(12–13), 1947–1957. https://doi.org/10.1016/j.jqsrt.2010.03.007 Kokaly, R. F., and Skidmore, A. K. (2015). Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm. International Journal of Applied Earth Observation and Geoinformation, 43, 55–83. https://doi.org/10.1016/j.jag.2015.01.010 Marshall, M., Belgiu, M., Boschetti, M., Pepe, M., Stein, A., and Nelson, A. (2022). Field-level crop yield estimation with PRISMA and Sentinel-2. ISPRS Journal of Photogrammetry and Remote Sensing, 187(February), 191–210. https://doi.org/10.1016/j.isprsjprs.2022.03.008 Tagliabue, G., Boschetti, M., Bramati, G., Candiani, G., Colombo, R., Nutini, F., Pompilio, L., Rivera-caicedo, J. P., Rossi, M., Rossini, M., Verrelst, J., and Panigada, C. (2022). Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 187(February), 362–377. https://doi.org/10.1016/j.isprsjprs.2022.03.014

• 자생식물 60종을 대상으로 꽃, 열매, 잎(앞면, 뒷면) 4가지 객체분류(식물부위)별로 직접 촬영한 이미지 데이터 • 자생식물 60종의 식물부위 중 기능 및 효능, 영양성분이 있는 부위에 대하여 채취 후 기능성분을 분석한 결과 데이터

- 종자 발아 데이터 : 5종 작물(벼, 밀, 고추, 배추, 무)별로 각 5개 품종에 대해 종이배지(발아객체 100립)기준으로 수집한 시계열 종자 발아 이미지 데이터 - 작물 품종 특성 데이터 : 4종 작물(고추, 무, 상추, 배추)별 각 10개 품종에 대한 6개 품종 특성 값을 판별한 기록 데이터

흰토마토에 대한 생육 데이터

장미(절화)의 품종별 재배환경/제어 데이터

딸기 내부환경 데이터

장미(절화)의 품종별 재배환경/제어 데이터