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All issues Volume 273 (2021) E3S Web Conf., 273 (2021) 01003 References
Open Access
Issue
E3S Web Conf.
Volume 273, 2021
XIV International Scientific and Practical Conference “State and Prospects for the Development of Agribusiness - INTERAGROMASH 2021”
Article Number 01003
Number of page(s) 11
Section Growing Cereal Crops and Plants
DOI https://doi.org/10.1051/e3sconf/202127301003
Published online 22 June 2021
  1. C. Li, X. Wang, Z. Meng, Tomato seeds maturity detection system based on chlorophyll fluorescence. Proc. SPIE 10021, Optical Design and Testing VII, 1002125 (2016) doi:10.1117/12.2247866 [Google Scholar]
  2. M. Kaszczuk, Z. Mierczyk, M. Zygmunt, Multispectral laser scanning in plants condition analysis. Proc. SPIE 10974, Laser Technology 2018: Progress and Applications of Lasers, 109740E doi: 10.1117/12.2516763 [Google Scholar]
  3. X. Wang, C. Li, Z. Meng, P. Fan, J. Cai, Pepper seed variety identification based on visible/near-infrared spectral technology. Proc. SPIE 10030, Infrared, Millimeter-Wave, and Terahertz Technologies IV, 100302N (2016) doi:10.1117/12.2247569 [Google Scholar]
  4. Y. Lu, R. Li, R. Lu, Detection of fresh bruises in apples by structured-illumination reflectance imaging. Proc. SPIE 9864, Sensing for Agriculture and Food Quality and Safety VIII, 986406 (2016) doi:10.1117/12.2225148 [Google Scholar]
  5. C. Lü, X. Jiang, X. Zhou, Y. Zhang, N. Zhang, C. Wei, W. Mao, Variable selection based near infrared spectroscopy quantitative and qualitative analysis on wheat wet gluten. Proc. SPIE 10461, AOPC 2017: Optical Spectroscopy and Imaging, 1046104 (2017) doi:10.1117/12.2281441 [Google Scholar]
  6. X. Song, J. Wang, X. Gu, X. Xu, Winter wheat GPC estimation with fluorescence-based sensor measurements of canopy. Proc. SPIE 9637, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, 96371L (2015) doi: 10.1117/12.2195289 [Google Scholar]
  7. J. Zhao, Y. Peng, S. Dhakal, L. Zhang, A. Sasao, A noninvasive technique for real-time detection of bruises in apple surface based on machine vision. Proc. SPIE 8721, Sensing for Agriculture and Food Quality and Safety V, 87210O (2013) doi:10.1117/12.2015897 [Google Scholar]
  8. F. Mendoza, R. Lu, H. Cen, Multi-sensor data fusion for improved prediction of apple fruit firmness and soluble solids content. Proc. SPIE 8027, Sensing for Agriculture and Food Quality and Safety III, 80270M (2011) doi: 10.1117/12.883649 [Google Scholar]
  9. D. Feng, J-W. Ji, L. Zhang, S.-J. Liu, Y-W. Tian, The research development of hyperspectral imaging in apple nondestructive detection and grading. Proc. SPIE 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology, 101560V (2016) doi:10.1117/12.2246537 [Google Scholar]
  10. M.A. Momin, N. Kondo, M. Kuramoto, Y. Ogawa, T. Shigi, Study on excitation and fluorescence spectrums of Japanese citruses to construct machine vision systems for acquiring fluorescent images. Proc. SPIE 8027, Sensing for Agriculture and Food Quality and Safety III, 80270R (2011) doi: 10.1117/12.885640 [Google Scholar]
  11. I. Baek, B. Cho, M. Kim, Y. Kim, Determination of optimal excitation and emission wavebands for detection of defect cherry tomato by using fluorescence emission and excitation matrix. Proc. SPIE 8721, Sensing for Agriculture and Food Quality and Safety V. 872108 (2013) doi:10.1117/12.2018543 [Google Scholar]
  12. W.-H. Su, S. Fennimore, D. Slaughter, Fluorescence imaging for rapid monitoring of translocation behaviour of systemic markers in snap beans for automated crop/weed discrimination. Biosystems Engineering 186, 156–167 (2019) https://doi.org/10.1016/j.biosystemseng.2019.07.009 [Google Scholar]
  13. J. Barbedo, E. Guarienti, C. Tibola, Detection of sprout damage in wheat kernels using NIR hyperspectral imaging Biosystems Engineering 175, 124–132 (2018) https://doi.org/10.1016/j.biosystemseng.2018.09.012 [Google Scholar]
  14. X. Xu, L. Xie, Y. Ying, Factors influencing near infrared spectroscopy analysis of agro-products: a review [J]. Front. Agr. Sci. Eng. 6(2), 105–115 (2019) https://doi.org/10.15302/J-FASE-2019255 [Google Scholar]
  15. D. Liu, W. Guo, Q. Li, D. Xie, Relationship of the bulk optical properties in 950–1650 nm wavelength range with internal quality and microstructure of kiwifruit during maturation. Biosystems Engineering 184, 45–54 (2019) doi:10.1016/j.biosystemseng.2019.05.005 [Google Scholar]
  16. A. Casson, R. Beghi, V. Giovenzana, I. Fiorindo, A. Tugnolo, R. Guidetti, Environmental advantages of visible and near infrared spectroscopy for the prediction of intact olive ripeness. Biosystems Engineering 189, 1–10 (2020). doi:10.1016/j.biosystemseng.2019.11.003 [Google Scholar]
  17. L. Yu, et al., Non-destructive identification of maize haploid seeds using nonlinear analysis method based on their near-infrared spectra. Biosystems Engineering 172, 144–153 (2018) [Google Scholar]
  18. P.-S. Liang, Nondestructive detection of zebra chip disease in potatoes using near-infrared spectroscopy Biosystems Engineering 166, 161–169 (2018) https://doi.org/10.1016/j.biosystemseng.2017.11.019 [Google Scholar]
  19. M. Li, D. Han, W. Liu, Non-destructive measurement of soluble solids content of three melon cultivars using portable visible/near infrared spectroscopy Biosystems Engineering 188, 31–39 (2019). https://doi.org/10.1016/j.biosystemseng.2019.10.003 [Google Scholar]
  20. J. Gené-Mola, et al., Fruit detection in an apple orchard using a mobile terrestrial laser scanner Biosystems Engineering 187, 171–184 (2019) https://doi.org/10.1016/j.biosystemseng.2019.08.017. [Google Scholar]
  21. V. Giovenzana, et al., Use of visible and near infrared spectroscopy with a view to online evaluation of oil content during olive processing Biosystems Engineering 172, 102–109 (2018) https://doi.org/10.1016/j.biosystemseng.2018.06.001. [Google Scholar]
  22. S. Moomkesh, S. Mireei, M. Sadeghi, M. Nazeri, Early detection of freezing damage in sweet lemons using Vis/SWNIR spectroscopy Biosystems Engineering 164, 157–170 (2017) https://doi.org/10.1016/j.biosystemseng.2017.10.009 [Google Scholar]
  23. X. Fu, X. Wang, X. Rao, An LED-based spectrally tuneable light source for visible and near-infrared spectroscopy analysis: A case study for sugar content estimation of citrus Biosystems Engineering 163, 87–93 (2017) https://doi.org/10.1016/j.biosystemseng.2017.08.022 [Google Scholar]
  24. V. Cortés, et al., Integration of simultaneous tactile sensing and visible and near-infrared reflectance spectroscopy in a robot gripper for mango quality assessment Biosystems Engineering 162, 112–123 (2017) https://doi.org/10.1016/j.biosystemseng.2017.08.005 [Google Scholar]
  25. M. V. Belyakov, V. O. Bulatikova, V. V. Dimkova, A. G. Dymnikova, Photoluminescent characteristics wheat seeds of different ripeness. XXXIII International scientific and practical conference «International scientific review of the problems and prospects of modern science and education». Boston, USA, International scientific review 4(35), 27–31 (2017) [Google Scholar]
  26. V. M. Belyakov, Photoluminescence monitoring of ripeness of seeds of grain during the ripening process. Inzhenernye tekhnologii i sistemy 2, 306–319 (2019) doi: 10.15507/2658-4123.029.201902.306-319 [Google Scholar]

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