Version 1
: Received: 17 September 2024 / Approved: 18 September 2024 / Online: 19 September 2024 (08:48:27 CEST)
How to cite:
Cruz-Carrasco, C.; Díaz-Álvarez, J.; Chávez de la O, F.; Sánchez-Venegas, A.; Villegas-Cortez, J. Detection of Aspergillus Flavus in Figs by Means of Hyperspectral Images and Deep Learning Algorithms. Preprints2024, 2024091364. https://doi.org/10.20944/preprints202409.1364.v1
Cruz-Carrasco, C.; Díaz-Álvarez, J.; Chávez de la O, F.; Sánchez-Venegas, A.; Villegas-Cortez, J. Detection of Aspergillus Flavus in Figs by Means of Hyperspectral Images and Deep Learning Algorithms. Preprints 2024, 2024091364. https://doi.org/10.20944/preprints202409.1364.v1
Cruz-Carrasco, C.; Díaz-Álvarez, J.; Chávez de la O, F.; Sánchez-Venegas, A.; Villegas-Cortez, J. Detection of Aspergillus Flavus in Figs by Means of Hyperspectral Images and Deep Learning Algorithms. Preprints2024, 2024091364. https://doi.org/10.20944/preprints202409.1364.v1
APA Style
Cruz-Carrasco, C., Díaz-Álvarez, J., Chávez de la O, F., Sánchez-Venegas, A., & Villegas-Cortez, J. (2024). Detection of Aspergillus Flavus in Figs by Means of Hyperspectral Images and Deep Learning Algorithms. Preprints. https://doi.org/10.20944/preprints202409.1364.v1
Chicago/Turabian Style
Cruz-Carrasco, C., Abel Sánchez-Venegas and Juan Villegas-Cortez. 2024 "Detection of Aspergillus Flavus in Figs by Means of Hyperspectral Images and Deep Learning Algorithms" Preprints. https://doi.org/10.20944/preprints202409.1364.v1
Abstract
Plant diseases not only cause economic losses, but they also pose a health risk. In this regard, aflatoxins are linked to the development of some type of liver cancer, especially the hepatocellular carcinoma. Aflatoxins are usually produced by the fungi Aspergillus flavus and Parsiticus in figs, and their identification is usually carried out using invasive and complex methods or by visual inspection at an advanced stage. Given that Spain is one of the main producers of figs and Extremadura is the leading region in the country, it is necessary to explore alternative ways of detecting the presence of aflatoxins in figs and preventing them from entering the food chain. The aim of this research is to address the early detection of Aspergillus flavus fungus using non-invasive techniques with the aid of hyperspectral imaging and the application of artificial intelligence techniques, in particular deep learning. The images were taken after inoculation of the microtoxin using 3 different concentrations, related to three different classes and healthy figs (healthy controls). The analysis of the hyperspectral images was performed at the pixel level. Firstly, a full connected neural network was used to analyse the spectral signature associated with each pixel; second, the wavelet transform was applied to each spectral signature. The resulting images are fed to a convolutional neural network. The hyperparameters of the proposed models were adjusted based on the parameter tuning process also performed. The results are promising, with 83% accuracy, 82.75% recall and 83.25% F1-measure for the single neural network, and 77.25% accuracy, recall and F1 for the convolution neural network.
Keywords
Agriculture; Deep Learning; Hyperspectral; imaging Artificial Neural Network; Precision agriculture
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
*
