| [1] |
Ji Y, Zhu L, Hao Z, Su S, Zheng X, et al. 2022. Exploring the Cunninghamia lanceolata (lamb.) Hook genome by bac sequencing. Frontiers in Bioengineering and Biotechnology 10:854130 doi: 10.3389/fbioe.2022.854130 |
| [2] |
Jiang Y, Hu Z, Han Z, Zhang J, Han S, Hao L. 2022. Growth characteristics of Cunninghamia lanceolata in China. Scientific Reports 12:18179 doi: 10.1038/s41598-022-22809-6 |
| [3] |
Tian Q, Zhang H, Bian L, Zhou L, Ge Y. 2024. Three-dimensional quantification and visualization of leaf chlorophyll content in poplar saplings under drought using SFM-MVS. Forests 15:20 doi: 10.3390/f15010020 |
| [4] |
Hao T, Han Y, Li Z, Yao H, Niu H. 2020. Estimating leaf chlorophyll content by laser-induced fluorescence technology at different viewing zenith angles. Applied Optics 59:7734−44 doi: 10.1364/AO.400032 |
| [5] |
Li W, Sun Z, Lu S, Omasa K. 2019. Estimation of the leaf chlorophyll content using multiangular spectral reflectance factor. Plant, Cell & Environment 42:3152−65 doi: 10.1111/pce.13605 |
| [6] |
Croft H, Chen JM, Luo X, Bartlett P, Chen B, et al. 2017. Leaf chlorophyll content as a proxy for leaf photosynthetic capacity. Global Change Biology 23:3513−24 doi: 10.1111/gcb.13599 |
| [7] |
Cheng T, Rivard B, Sánchez-Azofeifa AG, Féret JB, Jacquemoud S, et al. 2012. Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis. Journal of Plant Physiology 169:1134−42 doi: 10.1016/j.jplph.2012.04.006 |
| [8] |
Ullah S, Skidmore AK, Naeem M, Schlerf M. 2012. An accurate retrieval of leaf water content from mid to thermal infrared spectra using continuous wavelet analysis. Science of the Total Environment 437:145−52 doi: 10.1016/j.scitotenv.2012.08.025 |
| [9] |
Feng X, Zhan Y, Wang Q, Yang X, Yu C, et al. 2020. Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping. The Plant Journal 101:1448−61 doi: 10.1111/tpj.14597 |
| [10] |
Sarić R, Nguyen VD, Burge T, Berkowitz O, Trtílek M, et al. 2022. Applications of hyperspectral imaging in plant phenotyping. Trends in Plant Science 27:301−15 doi: 10.1016/j.tplants.2021.12.003 |
| [11] |
Burnett AC, Serbin SP, Davidson KJ, Ely KS, Rogers A. 2021. Detection of the metabolic response to drought stress using hyperspectral reflectance. Journal of Experimental Botany 72:6474−89 doi: 10.1093/jxb/erab255 |
| [12] |
Asaari MSM, Mertens S, Verbraeken L, Dhondt S, Inzé D, et al. 2022. Non-destructive analysis of plant physiological traits using hyperspectral imaging: a case study on drought stress. Computers and Electronics in Agriculture 195:106806 doi: 10.1016/j.compag.2022.106806 |
| [13] |
Ball KR, Liu H, Brien C, Berger B, Power SA, et al. 2022. Hyperspectral imaging predicts yield and nitrogen content in grass–legume polycultures. Precision Agriculture 23:2270−88 doi: 10.1007/s11119-022-09920-4 |
| [14] |
Lin M, Lynch V, Ma D, Maki H, Jin J, et al. 2022. Multi-species prediction of physiological traits with hyperspectral modeling. Plants 11:676 doi: 10.3390/plants11050676 |
| [15] |
Zhang C, Zhou L, Xiao Q, Bai X, Wu B, et al. 2022. End-to-end fusion of hyperspectral and chlorophyll fluorescence imaging to identify rice stresses. Plant Phenomics 2022:9851096 doi: 10.34133/2022/9851096 |
| [16] |
Mertens S, Verbraeken L, Sprenger H, Demuynck K, Maleux K, et al. 2021. Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology. Frontiers in Plant Science 12:640914 doi: 10.3389/fpls.2021.640914 |
| [17] |
Nagasubramanian K, Jones S, Singh AK, Sarkar S, Singh A, et al. 2019. Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant Methods 15:98 doi: 10.1186/s13007-019-0479-8 |
| [18] |
Zhang G, Xu T, Tian Y. 2022. Hyperspectral imaging-based classification of rice leaf blast severity over multiple growth stages. Plant Methods 18:123 doi: 10.1186/s13007-022-00955-2 |
| [19] |
Elmasry G, Kamruzzaman M, Sun D, Allen P. 2012. Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Critical Reviews in Food Science and Nutrition 52:999−1023 doi: 10.1080/10408398.2010.543495 |
| [20] |
Sendin K, Williams PJ, Manley M. 2018. Near infrared hyperspectral imaging in quality and safety evaluation of cereals. Critical Reviews in Food Science and Nutrition 58:575−90 doi: 10.1080/10408398.2016.1205548 |
| [21] |
Shorten PR, Leath SR, Schmidt J, Ghamkhar K. 2019. Predicting the quality of ryegrass using hyperspectral imaging. Plant Methods 15:63 doi: 10.1186/s13007-019-0448-2 |
| [22] |
Tang Y, Chen M, Wang C, Luo L, Li J, et al. 2020. Recognition and localization methods for vision-based fruit picking robots: a review. Frontiers in Plant Science 11:510 doi: 10.3389/fpls.2020.00510 |
| [23] |
Zhang H, Ge Y, Xie X, Atefi A, Wijewardane NK, et al. 2022. High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion. Plant Methods 18:60 doi: 10.1186/s13007-022-00892-0 |
| [24] |
Xiong J, Lin R, Bu R, Liu Z, Yang Z, et al. 2018. A micro-damage detection method of litchi fruit using hyperspectral imaging technology. Sensors 18:700 doi: 10.3390/s18030700 |
| [25] |
Pyo J, Duan H, Ligaray M, Kim M, Baek S, et al. 2020. An integrative remote sensing application of stacked autoencoder for atmospheric correction and cyanobacteria estimation using hyperspectral imagery. Remote Sensing 12:1073 doi: 10.3390/rs12071073 |
| [26] |
Liu W, Li Y, Tomasetto F, Yan W, Tan Z, et al. 2021. Non-destructive measurements of Toona sinensis chlorophyll and nitrogen content under drought stress using near infrared spectroscopy. Frontiers in Plant Science 12:809828 doi: 10.3389/fpls.2021.809828 |
| [27] |
Yang F, Tao L, Wang Q, Du M, Yang T, et al. 2021. Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters. Journal of Integrative Agriculture 20:2613−26 doi: 10.1016/S2095-3119(20)63306-8 |
| [28] |
Zhang J, Pan R, Gao W, Xu B, Li W. 2016. Automatic detection of layout of color yarns of yarn-dyed fabric. Part 2: region segmentation of double-system-mélange color fabric. Color Research & Application 41:626−35 doi: 10.1002/col.22003 |
| [29] |
He H, Chen Y, Li G, Wang Y, Ou X, et al. 2023. Hyperspectral imaging combined with chemometrics for rapid detection of talcum powder adulterated in wheat flour. Food Control 144:109378 doi: 10.1016/j.foodcont.2022.109378 |
| [30] |
Qu J, Sun D, Cheng J, Pu H. 2017. Mapping moisture contents in grass carp (Ctenopharyngodon idella) slices under different freeze drying periods by Vis-NIR hyperspectral imaging. LWT 75:529−36 doi: 10.1016/j.lwt.2016.09.024 |
| [31] |
Zhang J, Rivard B, Rogge DM. 2008. The Successive Projection Algorithm (SPA), an algorithm with a spatial constraint for the automatic search of endmembers in hyperspectral data. Sensors 8:1321−42 doi: 10.3390/s8021321 |
| [32] |
Araújo MCU, Saldanha TCB, Galvão RKH, Yoneyama T, Chame HC, et al. 2001. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics and Intelligent Laboratory Systems 57:65−73 doi: 10.1016/S0169-7439(01)00119-8 |
| [33] |
Li H, Liang Y, Xu Q, Cao D. 2009. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta 648:77−84 doi: 10.1016/j.aca.2009.06.046 |
| [34] |
Haghbin N, Bakhshipour A, Zareiforoush H, Mousanejad S. 2023. Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics. Plant Methods 19:53 doi: 10.1186/s13007-023-01032-y |
| [35] |
Wang Z, Fan S, Wu J, Zhang C, Xu F, et al. 2021. Application of long-wave near infrared hyperspectral imaging for determination of moisture content of single maize seed. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 254:119666 doi: 10.1016/j.saa.2021.119666 |
| [36] |
Martens H, Naes T. 1991. Multivariate calibration. New York: John Wiley & Sons. 440 pp. |
| [37] |
Park B, Seo Y, Yoon SC, Hinton A Jr, Windham WR, et al. 2015. Hyperspectral microscope imaging methods to classify gram-positive and gram-negative foodborne pathogenic bacteria. Transactions of the ASABE 58:5−16 doi: 10.13031/trans.58.10832 |
| [38] |
Zhang X, Sun J, Li P, Zeng F, Wang H. 2021. Hyperspectral detection of salted sea cucumber adulteration using different spectral preprocessing techniques and SVM method. LWT 152:112295 doi: 10.1016/j.lwt.2021.112295 |
| [39] |
Rasooli Sharabiani V, Soltani Nazarloo A, Taghinezhad E, Veza I, Szumny A, et al. 2023. Prediction of winter wheat leaf chlorophyll content based on VIS/NIR spectroscopy using ANN and PLSR. Food Science & Nutrition 11:2166−75 doi: 10.1002/fsn3.3071 |
| [40] |
Curcio JA, Petty CC. 1951. The near infrared absorption spectrum of liquid water. Journal of the Optical Society of America 41:302−04 doi: 10.1364/JOSA.41.000302 |
| [41] |
Huang H, Shen Y, Guo Y, Yang P, Wang H, et al. 2017. Characterization of moisture content in dehydrated scallops using spectral images. Journal of Food Engineering 205:47−55 doi: 10.1016/j.jfoodeng.2017.02.018 |
| [42] |
Maeda H, Ozaki Y, Tanaka M, Hayashi N, Kojima T. 1995. Near infrared spectroscopy and chemometrics studies of temperature-dependent spectral variations of water: relationship between spectral changes and hydrogen bonds. Journal of Near Infrared Spectroscopy 3:191−201 doi: 10.1255/jnirs.69 |
| [43] |
Rongtong B, Suwonsichon T, Ritthiruangdej P, Kasemsumran S. 2018. Determination of water activity, total soluble solids and moisture, sucrose, glucose and fructose contents in osmotically dehydrated papaya using near-infrared spectroscopy. Agriculture and Natural Resources 52:557−64 doi: 10.1016/j.anres.2018.11.023 |
| [44] |
Liu L, Zareef M, Wang Z, Li H, Chen Q, et al. 2023. Monitoring chlorophyll changes during Tencha processing using portable near-infrared spectroscopy. Food Chemistry 412:135505 doi: 10.1016/j.foodchem.2023.135505 |
| [45] |
Cui L, Wang X, Xu Y, Li Y, Han M. 2022. Hyperspectral reflectance imaging for water content and firmness prediction of potatoes by optimum wavelengths. Journal of Consumer Protection and Food Safety 17:51−64 doi: 10.1007/s00003-021-01343-z |
| [46] |
Martens H, Stark E. 1991. Extended multiplicative signal correction and spectral interference subtraction: new preprocessing methods for near infrared spectroscopy. Journal of Pharmaceutical and Biomedical Analysis 9:625−35 doi: 10.1016/0731-7085(91)80188-F |
| [47] |
Balabin RM, Safieva RZ, Lomakina EI. 2007. Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction. Chemometrics and Intelligent Laboratory Systems 88:183−88 doi: 10.1016/j.chemolab.2007.04.006 |
| [48] |
Feng S, Shang J, Tan T, Wen Q, Meng Q. 2023. Nondestructive quality assessment and maturity classification of loquats based on hyperspectral imaging. Scientific Reports 13:13189 doi: 10.1038/s41598-023-40553-3 |
| [49] |
Li D, Hu Q, Ruan S, Liu J, Zhang J, et al. 2023. Utilizing hyperspectral reflectance and machine learning algorithms for non-destructive estimation of chlorophyll content in citrus leaves. Remote Sensing 15:4934 doi: 10.3390/rs15204934 |
| [50] |
Zhang F, Zhang F, Wang S, Li L, Lv Q, et al. 2023. Hyperspectral imaging combined with cnn for maize variety identification. Frontiers in Plant Science 14:1254548 doi: 10.3389/fpls.2023.1254548 |
| [51] |
Oliveira MM, Cruz-Tirado JP, Roque JV, Teófilo RF, Barbin DF. 2020. Portable near-infrared spectroscopy for rapid authentication of adulterated paprika powder. Journal of Food Composition and Analysis 87:103403 doi: 10.1016/j.jfca.2019.103403 |
| [52] |
Sun J, Zhou X, Hu Y, Wu X, Zhang X, et al. 2019. Visualizing distribution of moisture content in tea leaves using optimization algorithms and NIR hyperspectral imaging. Computers and Electronics in Agriculture 160:153−59 doi: 10.1016/j.compag.2019.03.004 |
| [53] |
Zhang Y, Guo W. 2020. Moisture content detection of maize seed based on visible/near-infrared and near-infrared hyperspectral imaging technology. International Journal of Food Science & Technology 55:631−40 doi: 10.1111/ijfs.14317 |
| [54] |
Song Y, Cao S, Chu X, Zhou Y, Xu Y, et al. 2023. Non-destructive detection of moisture and fatty acid content in rice using hyperspectral imaging and chemometrics. Journal of Food Composition and Analysis 121:105397 doi: 10.1016/j.jfca.2023.105397 |
| [55] |
Li X, Wei Z, Peng F, Liu J, Han G. 2023. Non-destructive prediction and visualization of anthocyanin content in mulberry fruits using hyperspectral imaging. Frontiers in Plant Science 14:1137198 doi: 10.3389/fpls.2023.1137198 |
| [56] |
Sonobe R, Hirono Y, Oi A. 2020. Non-destructive detection of tea leaf chlorophyll content using hyperspectral reflectance and machine learning algorithms. Plants 9:368 doi: 10.3390/plants9030368 |
| [57] |
Raczko E, Zagajewski B. 2017. Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images. European Journal of Remote Sensing 50:144−54 doi: 10.1080/22797254.2017.1299557 |