Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods [PDF]
Frontiers in Plant Science, 2021 In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations.
Mohsen Yoosefzadeh-Najafabadi +4 more
doaj +3 more sources
Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges [PDF]
Plant Communications, 2021 Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and
James Schnable +2 more
exaly +3 more sources
Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean [PDF]
Frontiers in Plant Science, 2021 Recent substantial advances in high-throughput field phenotyping have provided plant breeders with affordable and efficient tools for evaluating a large number of genotypes for important agronomic traits at early growth stages.
Mohsen Yoosefzadeh-Najafabadi +4 more
doaj +3 more sources
A Hyperspectral Bidirectional Reflectance Model for Land Surface [PDF]
Sensors, 2020 A hyperspectral bidirectional reflectance (HSBR) model for land surface has been developed in this work. The HSBR model includes a very diverse land surface bidirectional reflectance distribution function (BRDF) database with ~40,000 spectra.
Xu Liu, Wan Wu
exaly +5 more sources
Separating Surface Reflectance from Volume Reflectance in Medical Hyperspectral Imaging [PDF]
DiagnosticsHyperspectral imaging has shown great promise for diagnostic applications, particularly in cancer surgery. However, non-bulk tissue-related spectral variations complicate the data analysis. Common techniques, such as standard normal variate normalization,
Lynn-Jade S. Jong +5 more
doaj +4 more sources
A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential [PDF]
Frontiers in Plant Science, 2020 To achieve rapid, accurate, and non-destructive diagnoses of nitrogen deficiency in cold land japonica rice, hyperspectral data were collected from field experiments to investigate the relationship between the nitrogen (N) content and the difference in ...
Fenghua Yu +12 more
doaj +3 more sources
Using hyperspectral reflectance to detect changes in photosynthetic activity in Atractylodes chinensis leaves as a function of decreasing soil water content [PDF]
PhotosyntheticaApplication of hyperspectral reflectance technology to track changes in photosynthetic activity in Atractylodes chinensis (A. chinensis) remains underexplored.
J. LIU +4 more
doaj +3 more sources
Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging. [PDF]
J Biomed Opt, 2022 . Significance Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis.
Witteveen M +5 more
europepmc +2 more sources
Hyperspectral reflectance dataset of pristine, weathered, and biofouled plastics
Earth System Science Data, 2023 . This work presents a hyperspectral reflectance dataset of macroplastic samples acquired using Analytical Spectral Devices (ASD) FieldSpec 4. Samples analyzed consisted of pristine, artificially weathered, and biofouled plastic items and plastic debris ...
Giuliano N. Leone +5 more
semanticscholar +5 more sources
Finite Element Analysis and Near-Infrared Hyperspectral Reflectance Imaging for the Determination of Blueberry Bruise Grading. [PDF]
Foods, 2022 Bruising of the subcutaneous tissues of blueberries is an important form of mechanical damage. Different levels of bruising have a significant effect on the post-harvest marketing of blueberries.
Zheng Z, An Z, Liu X, Chen J, Wang Y.
europepmc +2 more sources