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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics Communications in Science and Technology
Garces-Gomez, Yeison Alberto
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Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Image-based assessment of cattle manure-induced soil erosion in grazing systems Gómez-Guzmán, Cristian; Garcés-Gómez, Yeison Alberto
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5360-5370

Abstract

Extensive livestock farming significantly impacts soil erosion, necessitating accurate monitoring and assessment to mitigate environmental damage and enhance sustainable pasture management. This study employs unsupervised classification of high-resolution drone imagery to detect and quantify soil erosion associated with cattle manure in pastures, focusing on evaluating classification algorithms, identifying relevant spectral and textural features, and quantifying the extent and severity of erosion. The results demonstrate the effectiveness of unsupervised classification in identifying erosion zones and their impact on soil health and water quality. Field validation confirms the accuracy of the analysis, emphasizing the need for sustainable management practices such as controlled manure redistribution and soil conservation to mitigate erosion and protect natural resources. This approach offers practical tools for mitigating the environmental impacts of semi-extensive livestock farming and promoting more sustainable management. The findings provide practical recommendations for sustainable pasture management, contributing to environmental conservation and the long-term health of live-stock systems.
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 Alvarez-Rosero, Rosa Elvira Alzate, Valentina Cadavid Bolivar-Gomez, Fredy Alexander Cárdenas-Espinosa, Lorena Paola Cardona-Morales, Oscar Chirán-Alpala, William Efrén Corpas-Iguarán, Eduardo Corpas-Iguarán, Eduardo Javid Giraldo-García, Jaime Gómez-Guzmán, Cristian Gómez-Moya, Diego Alexander Henao-Cespedes, Vladimir Hernandez-Jorge, Freddy Eliseo Lara Escobar, Rubén Darío López-Mesa, Diana Jimena López-Naranjo, Oscar Montoya-Estrada, Claudia Nohemy Pinza-Jiménez, Christian Javier Reyes-Galván, Erick Salvador Rodríguez Ortiz, Angélica María Tapasco-Alzate, Omar Triviño-Valencia, Jessica Urbano-Cano, Astrid Lorena