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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.
Evaluation of artificial intelligence algorithms to estimate water quality parameters using satellite images Anaya-Valenzuela, Julio Cesar; Florez-Yepes, Gloria Yaneth; Garcés-Gómez, Yeison Alberto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp559-567

Abstract

The Ciénaga de la Virgen (Virgen Swamp) is a coastal lagoon in Cartagena de Indias that provides multiple ecosystem services in northern Bolívar. This ecosystem has faced anthropogenic pressure from city growth and improper water resource management, including wastewater and agrochemical discharges. Consequently, environmental authorities must monitor certain sites within the water body and extrapolate the data across its entire expanse. In this study, predictive tools are applied to determine water quality parameters such as chlorophyll-a (CL-a), dissolved oxygen (DO), total suspended solids (TSS), and salinity. This is achieved by correlating traditionally obtained data with the spectral response of medium-resolution satellite images, adjusted using artificial intelligence (AI) algorithms. Support vector machine (SVM) algorithms were used for regression, random forests (RF), and artificial neural networks (ANN), achieving an accuracy of 79% for CL-a, 95% for DO, 89% for TSS, and 96% for salinity. Validation was performed using mean absolute percentage error (MAPE) statistical metrics and root mean square error (RMSE).
Bibliometrics on the use of remote sensing and machine learning in crop classification Sánchez-Chavez, Andrea del Pilar; Henao-Cespedes, Vladimir; Garcés-Gómez, Yeison Alberto
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10476

Abstract

This study presents a bibliometric analysis of global research on crop classification using remote sensing and machine learning (ML), a field critical to advancing precision agriculture. A systematic search in Scopus identified 2,122 peer-reviewed articles published between 2014 and 2023. The analysis employed VOSviewer and the Bibliometrix package in R to assess publication trends, citation impact, and keyword co-occurrence networks. Results reveal a marked increase in scientific production after 2017, coinciding with the availability of high-resolution satellite imagery and the adoption of deep learning algorithms, particularly convolutional neural networks (CNNs). China emerged as the leading contributor, followed by the United States and India, reflecting strong investments in agricultural modernization and remote sensing infrastructure. Thematic mapping highlights both traditional research areas, such as vegetation indices and land cover classification, and emerging themes, including AI-supported algorithms and food security. Despite this growth, disparities persist, with most countries contributing fewer than 100 publications, underscoring the need to promote participation in underrepresented regions. Findings demonstrate the field’s rapid evolution, emphasize the integration of AI-driven methods in crop monitoring, and suggest future directions combining remote sensing, ML, and internet of things (IoT) technologies to address global challenges in food security and sustainable agricultural management.
Co-Authors Alvarez-Rosero, Rosa Elvira Alzate, Valentina Cadavid Anaya-Valenzuela, Julio Cesar Bolivar-Gomez, Fredy Alexander Cárdenas-Espinosa, Lorena Paola Cardona-Morales, Oscar Chirán-Alpala, William Efrén Corpas-Iguarán, Eduardo Corpas-Iguarán, Eduardo Javid Florez-Yepes, Gloria Yaneth 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 Sánchez-Chavez, Andrea del Pilar Tapasco-Alzate, Omar Triviño-Valencia, Jessica Urbano-Cano, Astrid Lorena