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Volume 14
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10.3390/electronics14020358
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Article

Innovative Machine Learning and Image Processing Methodology for Enhanced Detection of Aleurothrixus Floccosus

by
Manuel Alejandro Valderrama Solis
1,†,
Javier Valenzuela Nina
1,†,
German Alberto Echaiz Espinoza
2,*,†,
Daniel Domingo Yanyachi Aco Cardenas
2,†,
Juan Moises Mauricio Villanueva
3,†,
Andrés Ortiz Salazar
4,† and
Elmer Rolando Llanos Villarreal
5,†
1
Professional School of Engineering Telecommunications, Universidad Nacional de San Agustin de Arequipa, Arequipa 04002, Peru
2
Department of Engineering Electronics, Universidad Nacional de San Agustin de Arequipa, Arequipa 04002, Peru
3
Department of Electrical Engineering, Center for Alternative and Renewable Energies—CEAR Federal University of Paraíba (CEAR-UFPB), João Pessoa 58051-900, PB, Brazil
4
Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, RN, Brazil
5
Department of Natural Sciences, Mathematics, and Statistics, Federal Rural University of Semi-Arid (DCME-UFERSA), Mossoró 59625-900, RN, Brazil
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Electronics 2025, 14(2), 358; https://doi.org/10.3390/electronics14020358
Submission received: 1 December 2024 / Revised: 5 January 2025 / Accepted: 14 January 2025 / Published: 17 January 2025
(This article belongs to the Special Issue Innovations in Intelligent Agriculture: Advanced AI and Robotics for Modern Farming)
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Abstract

This paper presents a methodology for detecting the pest Aleurothrixus floccosus in citrus crops in Pedregal de Arequipa, Peru. The study employs simple random sampling during image collection to minimize bias, alternating and extracting leaves from different citrus trees. Image processing techniques, including noise reduction, edge smoothing, and segmentation, are applied for pest detection. Machine learning algorithms are used to classify the images, culminating in a robust detection methodology. A dataset of 1200 images was analyzed during the study.
Keywords: machine learning; random sampling; aleurothrixus floccosus; segmentation machine learning; random sampling; aleurothrixus floccosus; segmentation

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MDPI and ACS Style

Valderrama Solis, M.A.; Valenzuela Nina, J.; Echaiz Espinoza, G.A.; Yanyachi Aco Cardenas, D.D.; Villanueva, J.M.M.; Salazar, A.O.; Villarreal, E.R.L. Innovative Machine Learning and Image Processing Methodology for Enhanced Detection of Aleurothrixus Floccosus. Electronics 2025, 14, 358. https://doi.org/10.3390/electronics14020358

AMA Style

Valderrama Solis MA, Valenzuela Nina J, Echaiz Espinoza GA, Yanyachi Aco Cardenas DD, Villanueva JMM, Salazar AO, Villarreal ERL. Innovative Machine Learning and Image Processing Methodology for Enhanced Detection of Aleurothrixus Floccosus. Electronics. 2025; 14(2):358. https://doi.org/10.3390/electronics14020358

Chicago/Turabian Style

Valderrama Solis, Manuel Alejandro, Javier Valenzuela Nina, German Alberto Echaiz Espinoza, Daniel Domingo Yanyachi Aco Cardenas, Juan Moises Mauricio Villanueva, Andrés Ortiz Salazar, and Elmer Rolando Llanos Villarreal. 2025. "Innovative Machine Learning and Image Processing Methodology for Enhanced Detection of Aleurothrixus Floccosus" Electronics 14, no. 2: 358. https://doi.org/10.3390/electronics14020358

APA Style

Valderrama Solis, M. A., Valenzuela Nina, J., Echaiz Espinoza, G. A., Yanyachi Aco Cardenas, D. D., Villanueva, J. M. M., Salazar, A. O., & Villarreal, E. R. L. (2025). Innovative Machine Learning and Image Processing Methodology for Enhanced Detection of Aleurothrixus Floccosus. Electronics, 14(2), 358. https://doi.org/10.3390/electronics14020358

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