Morphological Characterization of Aspergillus flavus in Culture Media Using Digital Image Processing and Radiomic Analysis Under UV Radiation

Oscar J. Suarez (), Daniel C. Ruiz-Ayala, Liliana Rojas Contreras, Manuel G. Forero, Jesús A. Medrano-Hermosillo and Abraham Efraim Rodriguez-Mata
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Oscar J. Suarez: Ingeniería Mecatrónica, Facultad de Ingenierías y Arquitectura, Universidad de Pamplona, Km. 1 Vía Bucaramanga Ciudad Universitaria, Pamplona C.P. 543050, Norte de Santander, Colombia
Daniel C. Ruiz-Ayala: Doctorado en Automática, Facultad de Ingenierías y Arquitectura, Universidad de Pamplona, Km. 1 Vía Bucaramanga Ciudad Universitaria, Pamplona C.P. 543050, Norte de Santander, Colombia
Liliana Rojas Contreras: Grupo de Investigación GIMBIO, Microbiología, Facultad de Ciencias Básicas, Universidad de Pamplona, Km. 1 Vía Bucaramanga Ciudad Universitaria, Pamplona C.P. 543050, Norte de Santander, Colombia
Manuel G. Forero: Semillero Lún, Grupo D+Tec, Facultad de Ingeniería, Universidad de Ibague, Ibagué C.P. 730002, Tolima, Colombia
Jesús A. Medrano-Hermosillo: División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México Campus Chihuahua, Instituto Tecnológico de Chihuahua, Ave. Tecnológico #2909, Chihuahua C.P. 31310, Mexico
Abraham Efraim Rodriguez-Mata: División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México Campus Chihuahua, Instituto Tecnológico de Chihuahua, Ave. Tecnológico #2909, Chihuahua C.P. 31310, Mexico

Agriculture, 2025, vol. 15, issue 17, 1-23

Abstract: The identification of Aspergillus flavus ( A. flavus ), a fungus known for producing aflatoxins, poses a taxonomic challenge due to its morphological plasticity and similarity to closely related species. This article proposes a computational approach for its characterization across four culture media, using ultraviolet (UV) radiation imaging and radiomic analysis. Images were acquired with a camera controlled by a Raspberry Pi and processed to extract 408 radiomic features (102 per color channel and grayscale). Shapiro–Wilk and Levene’s tests were applied to verify normality and homogeneity of variances as prerequisites for an analysis of variance (ANOVA). Nine features showed statistically significant differences and, together with the culture medium type as a categorical variable, were used in a supervised classification stage with cross-validation. Classification using Support Vector Machines (SVM) achieved 97% accuracy on the test set. The results showed that the morphology of A. flavus varies significantly depending on the medium under UV radiation, with malt extract agar being the most discriminative. This non-invasive and low-cost approach demonstrates the potential of radiomics combined with machine learning to capture morphological patterns useful in the differentiation of fungi with optical response under UV radiation.

Keywords: image processing; computer-assisted; morphological features; feature selection; analysis of variance (ANOVA); statistical analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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