Abstract
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Zhiqi Liu, Jianxin Xue, Zhenyu Xie, Peizeng Wu
Abstract
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Confidence 0%
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10.1002/cem.785 · doi-reference
A well-conditioned estimator for large-dimensional covariance matrices
10.1016/s0047-259x(03)00096-4 · doi-reference
Review of the most common pre-processing techniques for near-infrared spectra
10.1016/j.trac.2009.07.007 · doi-reference
Multi-level data fusion strategy based on spectral and image information for identifying varieties of soybean seeds
10.1016/j.saa.2024.124815 · doi-reference
Viability discrimination and vigor estimation of naturally aged open-pollinated maize seeds via hyperspectral imaging
10.1016/j.microc.2025.116758 · doi-reference
Identification of maize seed vigor under different accelerated aging times using hyperspectral imaging and spectral deep features
10.1016/j.compag.2025.109980 · doi-reference
Modelling the vigour of maize seeds submitted to artificial accelerated ageing based on ATR-FTIR data and chemometric tools (PCA, HCA and PLS-DA)
10.1016/j.heliyon.2020.e03477 · doi-reference
Integration of hyperspectral imaging, non-targeted metabolomics and machine learning for vigour prediction of naturally and accelerated aged sweetcorn seeds
10.1016/j.foodcont.2023.109930 · doi-reference
The role of redox-active small molecules and oxidative protein post-translational modifications in seed aging
10.1016/j.plaphy.2024.108810 · doi-reference
Deterioration of orthodox seeds during ageing: influencing factors, physiological alterations and the role of reactive oxygen species
10.1016/j.plaphy.2020.11.031 · doi-reference
A rapid classification method for sorghum seed varieties based on HSI and PCA-SICNN algorithm
10.1016/j.microc.2024.111095 · doi-reference
Mapping hyperspectral NIR images using supervised self-organizing maps: discrimination of weedy rice seeds
10.1016/j.microc.2023.108599 · doi-reference
Nondestructive identification of barley seeds variety using near-infrared hyperspectral imaging coupled with convolutional neural network
10.1111/jfpe.13821 · doi-reference
Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
10.1111/ecog.02881 · doi-reference
On the sampling strategy for evaluation of spectral-spatial methods in hyperspectral image classification
10.1109/tgrs.2016.2616489 · doi-reference
Being aware of data leakage and cross-validation scaling in chemometric model validation
10.1002/cem.70026 · doi-reference
Machine learning of Raman spectroscopic data: comparison of different validation strategies
10.1002/jrs.6842 · doi-reference
Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling
10.1038/s41596-021-00620-3 · doi-reference
Covariate balanced allocation of samples to batches to mitigate the impacts of technical variability
10.1093/bioadv/vbag048 · doi-reference
Research on multi modal corn seed vitality grading based on three branch cross attention fusion network
10.1016/j.saa.2026.127828 · doi-reference
Research on nondestructive detection of sweet-waxy corn seed varieties and mildew based on stacked ensemble learning and hyperspectral feature fusion technology
10.1016/j.saa.2024.124816 · doi-reference
A lightweight dual-channel feature fusion model for wheat variety identification in hyperspectral images
10.1016/j.saa.2026.127595 · doi-reference
Research on variety identification of common bean seeds based on hyperspectral and deep learning
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An automatic 3-D ultrasound and photoacoustic combined imaging system for human inflammatory arthritis
10.1109/tuffc.2023.3290824 · doi-reference
Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping
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Determination of geographic origins and types of Lindera aggregata samples using a portable short-wave infrared hyperspectral imager
10.1016/j.saa.2022.121370 · doi-reference
Machine learning classification of origins and varieties of Tetrastigma hemsleyanum using a dual-mode microscopic hyperspectral imager
10.1016/j.saa.2021.120054 · doi-reference
Incoherent broadband cavity-enhanced absorption spectroscopy for sensitive measurement of nutrients and microalgae
10.1364/ao.449467 · doi-reference
Reliability analysis of photoluminescence measurement data from the intrinsic limitation on the instrument to the practical limitations during the experiments
10.1016/j.saa.2025.127107 · doi-reference
Multi-mode microscopic hyperspectral imager for the sensing of biological samples
10.3390/app10144876 · doi-reference
Confocal hyperspectral microscopic imager for the detection and classification of individual microalgae
10.1364/oe.438253 · doi-reference