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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Hernandez-Jorge, Freddy Eliseo
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Computer Science & IT Engineering

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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 Cardona-Morales, Oscar Garces-Gomez, Yeison Alberto López-Naranjo, Oscar Montoya-Estrada, Claudia Nohemy