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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Cardona-Morales, Oscar
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Remote sensing in the analysis of the behavior of CO associated with confinement due to COVID-19, in the city of Manizales Henao-Céspedes, Vladimir; Garcés-Gómez, Yeison Alberto; Cardona-Morales, Oscar
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7441

Abstract

This article analyzed the behavior of carbon monoxide (CO) levels in Manizales during pre-lockdown, lockdown, and post-lockdown, as a response to the coronavirus disease (COVID-19) pandemic. The analysis focuses on the data of CO levels obtained from the tropospheric monitoring instrument (TROPOMI), precipitation, and temperature (T) recorded by the network of stations of Caldas. The data allowed us to find that during the lockdown, the average value of CO was 9.92% lower than the value registered before the lockdown, and it was 11.75% lower after the lockdown. On the other hand, the correlation between CO levels and population density during the three periods was analyzed, obtaining an ?2 = 0.816 after lockdown. Finally, considering other possible variables that can affect the CO levels, an analysis of the behavior of CO was carried out concerning the temperature and precipitation of the city registered before, during, and after the lockdown. Regarding CO and temperature, the correlation was inverse with Pearson’s ? = −0.599 (Fisher’s ? = −0.692), which also supports the decreasing trend of the value measured, and that the variation of CO levels does not depend only on lockdown but also on other factors. Regarding CO and precipitation, a positive correlation of Pearson’s ? = 0.165 (Fisher’s ? = 0.167) was obtained.
Early detection of tar spot disease in Zea mays using hyperspectral reflectance and machine learning Montoya-Estrada, Claudia Nohemy; Cardona-Morales, Oscar; López-Naranjo, Oscar; Hernandez-Jorge, Freddy Eliseo; Garcés-Gómez, Yeison Alberto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4722-4730

Abstract

Ensuring food security and meeting the economic needs of farmers and nations depend heavily on detecting and preventing crop yield losses. Early detection of tar spot caused by Phyllachora maydis is crucial to implementing efficient mitigation actions in the earliest stages of infestation. Currently, visual methods are used for detection, which require extensive training and experience from the operator. However, remote sensing techniques can be used to detect tar spot infestation through the selection of wavelengths present in the maize plant spectral signature. This research proposes using machine learning techniques and logistic regression to determine the first stage of tar spot infestation. The results show that the logistic regression model is the most suitable for detecting this first stage, and the K-Nearest Neighbors Classification and Random Forest Classification algorithms generate the best classification results. This approach can significantly reduce costs in terms of time, labor, and subjective analysis.
Co-Authors Garces-Gomez, Yeison Alberto Henao-Cespedes, Vladimir Hernandez-Jorge, Freddy Eliseo López-Naranjo, Oscar Montoya-Estrada, Claudia Nohemy