Classification of Hass avocado (persea americana mill) in terms of its ripening via hyperspectral images
The use of non-invasive and low-cost methodologies allows the monitoring of fruit ripening and quality control, without affecting the product under study. In particular, the Hass avocado is of high importance for the agricultural sector in Colombia because the country is strongly promoting its export, which has generated an expansion in the number of acres cultivated with this fruit. Therefore, this paper aims to study and analyze the ripening state of Hass avocados through non-invasive hyperspectral images, using principal component analysis (PCA) along with spectral vegetation indices, such as the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), photochemical reflectance index (PRI), colorimetry analysis in the CIE L*a*b* color space, and color index triangular greenness index (TGI). In particular, this work conducts a quantitative analysis of the ripening process of a population of 7 Hass avocados over 10 days. The avocados under study were classified into three categories: unripe, close-to-ripe, and ripe. The obtained results show that it is possible to characterize the ripening state of avocados through hyperspectral images using a non-invasive acquisition system. Further, it is possible to know the post-harvest ripening state of the avocado at any given day.
J. Barrault, M. Boisseau, Y. Pouilloux, and A. Piccirilli, “Method for preparing a fatty substance ester and use thereof in pharmaceutics, cosmetics or food industry,” 6,828,451,7, 2004.
M. del M. Cerdas Araya, M. Montero Calderón, and O. Somarribas Jones, “Verificación del contenido de materia seca como indicador de cosecha para aguacate (Persea americana) cultivar Hass en zona intermedia de producción de Los Santos, Costa Rica,” Agron. Costarric., vol. 38, no. 1, pp. 207–214, 2014.
M. del M. Cerdas Araya, M. Montero Calderón, and E. Díaz Cordero, “Manual de manejo pre y poscosecha de aguacate (Persea americana),” 2006.
K. A. Cox, T. K. McGhie, A. White, and A. B. Woolf, “Skin colour and pigment changes during ripening of ‘Hass’ avocado fruit,” Postharvest Biol. Technol., vol. 31, no. 3, pp. 287–294, Mar. 2004. https://doi.org/10.1016/j.postharvbio.2003.09.008.
P. M. A. Toivonen and D. A. Brummell, “Biochemical bases of appearance and texture changes in fresh-cut fruit and vegetables,” Postharvest Biol. Technol., vol. 48, no. 1, pp. 1–14, Apr. 2008. https://doi.org/10.1016/j.postharvbio.2007.09.004.
M. L. Hertog, S. E. Nicholson, and K. Whitmore, “The effect of modified atmospheres on the rate of quality change in ‘Hass’ avocado,” Postharvest Biol. Technol., vol. 29, no. 1, pp. 41–53, Jul. 2003. https://doi.org/10.1016/S0925-5214(02)00211-9.
S. Ochoa-Ascencio, M. L. Hertog, and B. M. Nicolaï, “Modelling the transient effect of 1-MCP on ‘Hass’ avocado softening: A Mexican comparative study,” Postharvest Biol. Technol., vol. 51, no. 1, pp. 62–72, Jan. 2009. https://doi.org/10.1016/j.postharvbio.2008.06.002.
M. M. CERDAS, G. UMAÑA, and A. SÁENZ, “Documento respaldo para la elaboración del Reglamento Oficial de Aguacate (Persea americana),” Lab. Poscosecha, CIA, UCR. San José, CR, vol. 38, no. 1, p. 8, 2010.
E. Hurtado-Fernández, A. Fernández-Gutiérrez, and A. Carrasco-Pancorbo, “Avocado fruit— Persea americana,” in Exotic Fruits, F. Federal University of Ceará, Ed. Ceará, Brazil: Elsevier, 2018. https://doi.org/10.1016/B978-0-12-803138-4.00001-0, pp. 37–48.
A. Hussain, H. Pu, and D.-W. Sun, “Innovative nondestructive imaging techniques for ripening and maturity of fruits – A review of recent applications,” Trends Food Sci. Technol., vol. 72, pp. 144–152, Feb. 2018. https://doi.org/10.1016/j.tifs.2017.12.010.
N. T. Vetrekar et al., “Non-invasive hyperspectral imaging approach for fruit quality control application and classification: case study of apple, chikoo, guava fruits,” J. Food Sci. Technol., vol. 52, no. 11, pp. 6978–6989, Nov. 2015. https://doi.org/10.1007/s13197-015-1838-8.
M. L. Stone, P. R. Armstrong, X. Zhang, G. H. Brusewitz, and D. D. Chen, “Watermelon Maturity Determination in the Field Using Acoustic Impulse Impedance Techniques,” Trans. ASAE, vol. 39, no. 6, pp. 2325–2330, 1996. https://doi.org/10.13031/2013.27743.
H. Q. Yang, “Nondestructive Prediction of Optimal Harvest Time of Cherry Tomatoes Using VIS-NIR Spectroscopy and PLSR Calibration,” Adv. Eng. Forum, vol. 1, pp. 92–96, Sep. 2011. https://doi.org/10.4028/www.scientific.net/AEF.1.92.
S. S. Sivakumar, J. Qiao, N. Wang, Y. Gariépy, G. S. V Raghavan, and J. McGill, “Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction—A Review,” Plants, vol. 7, no. 1, p. 3, Jan. 2018. https://doi.org/10.3390/plants7010003.
D. Haboudane, N. Tremblay, J. R. Miller, and P. Vigneault, “Remote Estimation of Crop Chlorophyll Content Using Spectral Indices Derived From Hyperspectral Data,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 2, pp. 423–437, Feb. 2008. https://doi.org/10.1109/TGRS.2007.904836.
D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Lab. J., vol. 14, no. 1, pp. 79–116, 2003.
A. F. Jiménez-López, F. R. Jiménez-López, and M. Jiménez-López, “Multispectral analysis of vegetation for remote sensing applications,” Iteckne, vol. 12, no. 2, pp. 156–167, 2015.
E. R. Hunt, P. C. Doraiswamy, J. E. McMurtrey, C. S. T. Daughtry, E. M. Perry, and B. Akhmedov, “A visible band index for remote sensing leaf chlorophyll content at the canopy scale,” Int. J. Appl. Earth Obs. Geoinf., vol. 21, pp. 103–112, Apr. 2013. https://doi.org/10.1016/j.jag.2012.07.020.
J. Xue and B. Su, “Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications,” J. Sensors, vol. 2017, pp. 1–17, 2017. https://doi.org/10.1155/2017/1353691.
G. M. Gandhi, S. Parthiban, N. Thummalu, and A. Christy, “Ndvi: Vegetation Change Detection Using Remote Sensing and Gis – A Case Study of Vellore District,” Procedia Comput. Sci., vol. 57, pp. 1199–1210, 2015. https://doi.org/10.1016/j.procs.2015.07.415.
Y. Tan, J.-Y. Sun, B. Zhang, M. Chen, Y. Liu, and X.-D. Liu, “Sensitivity of a Ratio Vegetation Index Derived from Hyperspectral Remote Sensing to the Brown Planthopper Stress on Rice Plants,” Sensors, vol. 19, no. 2, p. 375, Jan. 2019. https://doi.org/10.3390/s19020375.
M. F. Garbulsky, J. Peñuelas, J. Gamon, Y. Inoue, and I. Filella, “The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficienciesA review and meta-analysis,” Remote Sens. Environ., vol. 115, no. 2, pp. 281–297, Feb. 2011. https://doi.org/10.1016/j.rse.2010.08.023.
Y. Liu, D.-W. Sun, J.-H. Cheng, and Z. Han, “Hyperspectral Imaging Sensing of Changes in Moisture Content and Color of Beef During Microwave Heating Process,” Food Anal. Methods, vol. 11, no. 9, pp. 2472–2484, Sep. 2018. https://doi.org/10.1007/s12161-018-1234-x.
K. León, D. Mery, F. Pedreschi, and J. León, “Color measurement in L∗a∗b∗ units from RGB digital images,” Food Res. Int., vol. 39, no. 10, pp. 1084–1091, Dec. 2006. https://doi.org/10.1016/j.foodres.2006.03.006.
W. Castro, J. Oblitas, M. De-La-Torre, C. Cotrina, K. Bazan, and H. Avila-George, “Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces,” IEEE Access, vol. 7, pp. 27389–27400, 2019. https://doi.org/10.1109/ACCESS.2019.2898223.
I. Arzate-Vázquez et al., “Image Processing Applied to Classification of Avocado Variety Hass (Persea americana Mill.) During the Ripening Process,” Food Bioprocess Technol., vol. 4, no. 7, pp. 1307–1313, Oct. 2011. https://doi.org/10.1007/s11947-011-0595-6.
E. R. Hunt, C. S. . T. Daughtry, J. U. H. Eitel, and D. S. Long, “Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index,” Agron. J., vol. 103, no. 4, p. 1090, 2011. https://doi.org/10.2134/agronj2010.0395.
D. H. Foster, K. Amano, S. M. C. Nascimento, and M. J. Foster, “Frequency of metamerism in natural scenes,” J. Opt. Soc. Am. A, vol. 23, no. 10, pp. 2359–2357, Oct. 2006. https://doi.org/10.1364/JOSAA.23.002359.
Copyright (c) 2019 TecnoLógicas
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The texts published in this magazine, as of June of the year 2018, are under a Creative Commons License "Recognition-Non-Commercial-Share Equal" that allows others: