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Early detection of tar spot disease in Zea mays using hyperspectral reflectance and machine learning

IAES International Journal of Artificial Intelligence · Vol. 14 No. 6 (2025) · hlm. 4722–4730

DOI
10.11591/ijai.v14.i6.pp4722-4730
ISSN
2252-8938, 2089-4872
Terbit
2025-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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  • Universidad Católica de Manizales
  • Universidad Nacional de Colombia
  • Universidad de Caldas
  • Universidad del Cauca
  • Universidad Autonoma de Manizales
  • Universidad de la Amazonia
  • Nigerian Institute For Oil Palm Reasearch
  • Organisasi lain
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  • Organisasi berbeda
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Vladimir Henao-CéspedesUniversidad Católica de Manizales6
Nicolás Toro GarcíaUniversidad Nacional de Colombia5
Fredy E. HoyosUniversidad Nacional de Colombia3
Rubén Darío Lara-EscobarUniversidad Católica de Manizales2
Paula Andrea López JimenezUniversidad Católica de Manizales2
Eduardo Javid Corpas IguaránUniversidad Católica de Manizales2
Omar Alberto Tapasco AlzateUniversidad de Caldas2
LUIS FERNANDO DIAZ CADAVIDUniversidad Nacional de Colombia1
Angie Marcela Galvez ValenciaAfiliasi tidak diketahui1
Erwin Leandro Lemus RodriguezAfiliasi tidak diketahui1
Miguel Andres Arango ArgotiAfiliasi tidak diketahui1
José Israel Cardenas-JimenezUniversidad Nacional de Colombia1
Carlos-Augusto González-CorreaUniversidad de Caldas1
Clara Helena González-CorreaUniversidad de Caldas1
María Nancy Marín OlayaUniversidad Católica de Manizales1
Jesús Emilio Pinto LoperaUniversidad de la Amazonia1
Diana Carolina Meneses-CabezasUniversidad Católica de Manizales1
Yuliana Zapata-SernaUniversidad Católica de Manizales1
Jose Manuel Monsalve-TellezUniversidad Católica de Manizales1
Jorge Luís Torres-LeónNigerian Institute For Oil Palm Reasearch1
Christian Javier Pinza-JiménezUniversidad Católica de Manizales1
Astrid Lorena Urbano-CanoUniversidad del Cauca1
Diana Jimena López MesaCorporación Universitaria Comfacauca - Unicomfacauca1
Rosa Elvira Álvarez RoseroUniversidad del Cauca1
William Efrén Chirán-AlpalaUniversidad Católica de Manizales1
Lorena Paola Cárdenas EspinosaUniversidad Católica de Manizales1
Diego Alexander Gómez-MoyaAfiliasi tidak diketahui1
Laura Valeria Avendaño-GarcíaUniversidad Católica de Manizales1
Cristian Gómez-GuzmánUniversidad Católica de Manizales1
Erick Salvador Reyes-GalvánUniversidad Católica de Manizales1
Fredy Alexander Bolivar-GomezAfiliasi tidak diketahui1
Gloria Matilde Gil DuqueUniversidad Católica de Manizales1
Jessica Triviño-ValenciaUniversidad Católica de Manizales1
Jaime Alberto Giraldo-GarcíaUniversidad Nacional de Colombia1
Julio Cesar Anaya-ValenzuelaUniversidad Católica de Manizales1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Early detection of tar spot disease in Zea mays using hyperspectral reflectance and machine learning</dc:title>
	<dc:creator>Montoya-Estrada, Claudia Nohemy</dc:creator>
	<dc:creator>Cardona-Morales, Oscar</dc:creator>
	<dc:creator>López-Naranjo, Oscar</dc:creator>
	<dc:creator>Hernandez-Jorge, Freddy Eliseo</dc:creator>
	<dc:creator>Garcés-Gómez, Yeison Alberto</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Machine learning classification;  Phyllachora maydis;  Spectral signature;  Tar spot;  Zea mays</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/26407</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i6.pp4722-4730</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 6: December 2025; 4722-4730</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i6</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/26407/14865</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Claudia Nohemy Montoya-Estrada, Oscar Cardona-Morales, Oscar López-Naranjo, Freddy Eliseo Hernandez-Jorge, Yeison Alberto Garcés-Gómez</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>