Differentiation of storage time of wheat seed based on near infrared hyperspectral imaging
Abstract
Keywords: hyperspectral image, wheat seed, storage, intelligent monitoring, single seed
DOI: 10.3965/j.ijabe.20171002.1619
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Madhava Roa K V, Kalpana R. Carbohydrates and the ageing process in seeds of pigeonpea cultivars (cajanus cajan L.). Seed science and technology, 1994; 22: 495–501.
Lv Y, Zhang S, Wang J, Hu Y. Quantitative proteomic analysis of wheat seeds during artificial ageing and priming using the isobaric tandem mass tag labeling. PLoS ONE, 2016; 11(9): e0162851. doi: 10.1371/journal.pone.0162851.
Ferreira R L, Novembre, A D D L C. Estimate of vigour in seeds and seedling of Bixa orellana L. Revista Ciencia Agronomica, 2016; 47(1): 101–107.
Hammed A, Goher M, lqbal N. Evaluation of seedling survivability and growth response as selection criteria for breeding drought. Cereal Research Communications, 2010; 28(2): 193–202.
Cozzolino D. An overview of the use of infrared spectroscopy and chemometrics inauthenticity and traceability of cereals. Food Research International, 2014; 60: 262–265.
Zhao H Y, Guo B L, Wei Y M, Zhang B. Effects of grown origin, genotype, harvest year, and their interactions of wheat kernels on near infrared spectral fingerprints for geographical traceability. Food Chemistry, 2014; 152: 316–322.
Liu F, Li T, Liu G. Infrared spectroscopic study of wheat and red kidney beans of different storage times. The Journal of Light Scattering, 2010; 22(2): 186–189. (in Chinese)
Li J, Li ZH, Fu X J. Study on rapid non-destructive detection of the freshness of paddy based on NIRS. Spectroscopy and Spectral Analysis, 2012; 32(8): 2126–2130. (in Chinese)
An D, Cui Y, Liu X, Jia S, Zheng S, Che X, Liu Z, Zhang X, Zhu D, Li S. Effects of Varieties, Producing Areas, Ears, and Ear Positions of Single Maize Kernels on Near-Infrared Spectra for Identification and Traceability. PLoS ONE, 2016; 11(9): e0161489. doi: 10.1371/journal.pone.0161489.
Liu Y L, Lyu Q, He S L. Prediction of nitrogen and phosphorus contents in citrus leaves based on hyperspectral imaging. Int J Agric & Biol Eng, 2015; 8(2): 80–88.
Mahesh S, Jayas D S, Paliwal J, White N D G. Hyperspectral imaging to classify and monitor quality of agricultural materials. Journal of Stored Products Research, 2015; 61: 17–26.
Rajkumar P, Wang N, EImasry G. Studies on banana fruit quality and maturity stages using hyperspectral imaging. Journal of Food Engineering, 2012; 108(1): 94–200.
Lee H, Kim M S, Jeong D, Delwiche S R, Chao K, Cho B K. Detection of cracks on tomatoes using a hyperspectral near-infrared reflectance imaging system. Sensors; 2014, 14(10): 18837–18850.
Wang Y, Xu C B, Han L. Studies on seed vigor and physiological indications of different storage duration elymus sibiricus. Seed, 2012; 31(8): 13–18.
Zhu D Z, Wang C, Pang B S, Shan F H, Wu Q, Zhao C J. Identification of wheat cultivars based on hyperspectral image of single seed. Journal of Nanoletectronics and Optoelectronics, 2012; 7(2): 167–171.
Hung P V, Symons S, Shahin M, Hatcher D. Using a short wavelength infrared (SWIR) hyperspectral imaging system to predict alpha amylase activity in individual Canadian western wheat kernels. Sensing and Instrumentation for Food Quality and Safety, 2009; 3(4): 211–218.
Wu J Z, Wu S N, Liu C L, Chen X H, Gao F. Explorations of wheat grain protein content predication using NIR and hyperspectrum technology. Transducer and Microsystem Technologie, 2013; 32(2): 60–62.
Vermeulen P, Fernández Pierna J A, van Egmond H P, Zegers J, Dardenne P, Baeten V. Validation and transferability study of a method based on near-infrared hyperspectral imaging for the detection and quantification of ergot bodies in cereals. Analytical and Bioanalytical Chemistry, 2013; 405(24): 7765–7772.
Liang K, Du Y Y, Lu W, Wang G, Xu J H, Shen M X. Identification of Fusarium head blight wheat based on Hyperspectral imaging technology. Transactions of the CSAM, 2016; 47(2): 309–315. (in Chinese with English abstract)
Varzakas T. Quality and safety aspects of cereals (wheat) and their products. Critical Reviews in Food Science and Nutrition, 2016; 56: 2495–2510.
Wu J Z, Liu Q, Chen Y, Liu C L. Prediction method of single wheat grain protein content based on hyperspectral image. Infrared and Laser Engineering, 2016; 45(S1): S123002-1, S123002-5. (in Chinese)
Bajorski P. Statistical inference in PCA for hyperspectral images. IEEE Journal of Selected Topics in Signal Processing, 2011; 5(3): 438–445.
Farrell M D, Mersereau R M. On the impact of PCA dimension reduction for hyperspectral detection of difficult targets. IEEE Geoscience and Remote Sensing Letters, 2005; 2(2): 192–195.
Kim D W, Burks D F, Ritenour M A. Citrus black spot detection using hyperspectral imaging. Int J Agric & Biol Eng, 2014; 7(6): 20–17.
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