Calyx and stem discrimination for apple quality control using hyperspectral imaging

Israel Pineda, Nur Alam MD, Oubong Gwun

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

7 Citas (Scopus)


The production of high-quality food products needs an efficient method to detect defects in food, this is particularly true in the production of apples. Hyperspectral image processing is a popular technique to carry out this detection. However, the stem and calyx of the apple provoke frequent detection errors. We analyze the spectrum of our apple data set, propose an algorithm that uses the average of the principal components of two regions of the spectrum to identify the defects, and couple this detection routine with a two-band ratio that discriminates the calyx and stem. Our study considers the spectral range between 403 nm and 998 nm. Our results include the detection of scab, bruise, crack, and cut with and without stem and calyx. We describe all the necessary parameters provided by our spectral analysis. Our algorithm has an overall accuracy of 95%. We conclude that our algorithm effectively detects defects in the presence of stem and calyx.

Idioma originalInglés
Título de la publicación alojadaTechnology Trends - 4th International Conference, CITT 2018, Revised Selected Papers
EditoresMiguel Botto-Tobar, Mayra D’Armas, Miguel Zúñiga Sánchez, Miguel Zúñiga-Prieto, Guillermo Pizarro
EditorialSpringer Verlag
Número de páginas14
ISBN (versión impresa)9783030055318
EstadoPublicada - 2019
Publicado de forma externa
Evento4th International Conference on Technology Trends, CITT 2018 - Babahoyo, Ecuador
Duración: 29 ago. 201831 ago. 2018

Serie de la publicación

NombreCommunications in Computer and Information Science
ISSN (versión impresa)1865-0929


Conferencia4th International Conference on Technology Trends, CITT 2018


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