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Recycling
Volume 11
Issue 5
10.3390/recycling11050086
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Comparative Analysis of Techniques for Texture Feature Extraction for Supervised Classification of Wood and Textile Waste

by
Wilfrido Campos Francisco
1,
Jonathan Villanueva Tavira
2,*,
Jonathan Jesús Carranza Vega
1,
Blanca Dina Valenzuela Robles
2,
Erik Rosado Tamariz
2 and
Andrés Blanco Ortega
2
1
Technological Institute of Chilpancingo, National Technological Institute of Mexico, Chilpancingo de los Bravo 39090, Guerrero, Mexico
2
National Center for Research and Technological Development, National Technological Institute of Mexico, Cuernavaca 62493, Morelos, Mexico
*
Author to whom correspondence should be addressed.
Recycling 2026, 11(5), 86; https://doi.org/10.3390/recycling11050086
Submission received: 13 March 2026 / Revised: 14 April 2026 / Accepted: 30 April 2026 / Published: 5 May 2026
Versions Notes

Abstract

Municipal Solid Waste (MSW) is a common problem in all cities worldwide; it is expected to increase to 3400 billion tons by 2050. In Mexico, an average of 108,146 tons of MSW are generated daily. Artificial Intelligence (AI) is a computer tool that allows the development of systems that facilitate the recycling process. However, most AI programs focus on classifying paper, plastic, glass and metal; therefore, wood and textile waste have received little attention. Using texture techniques such as Local Binary Pattern (LBP), Gray-Level Co-occurrence Matrix (GLCM), Histogram of Oriented Gradients (HOG), Canny/Sobel edge detection, Fractal Dimension (FD), feature values were extracted and integrated from 4396 images belonging to wood and textile categories. Using the Random Forest Importance method, the most significant features were selected to train three Machine Learning (ML) algorithms. Multilayer Perceptron (MLP) achieved the best performance in accuracy with 96.70%, followed by Random Forest (RF) at 95.45% and Support Vector Machine (SVM) with 95.22%. The implementation of these comparisons will serve as a basis for the development of new technological tools with low computational cost that carry out a proper waste separation.
Keywords: Municipal Solid Waste; Artificial Intelligence; Wood Waste; textile waste; texture feature; Machine Learning; Artificial Neural Network; Support Vector Machines; Random Forest; Multilayer Perceptron Municipal Solid Waste; Artificial Intelligence; Wood Waste; textile waste; texture feature; Machine Learning; Artificial Neural Network; Support Vector Machines; Random Forest; Multilayer Perceptron

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MDPI and ACS Style

Francisco, W.C.; Tavira, J.V.; Vega, J.J.C.; Robles, B.D.V.; Tamariz, E.R.; Ortega, A.B. Comparative Analysis of Techniques for Texture Feature Extraction for Supervised Classification of Wood and Textile Waste. Recycling 2026, 11, 86. https://doi.org/10.3390/recycling11050086

AMA Style

Francisco WC, Tavira JV, Vega JJC, Robles BDV, Tamariz ER, Ortega AB. Comparative Analysis of Techniques for Texture Feature Extraction for Supervised Classification of Wood and Textile Waste. Recycling. 2026; 11(5):86. https://doi.org/10.3390/recycling11050086

Chicago/Turabian Style

Francisco, Wilfrido Campos, Jonathan Villanueva Tavira, Jonathan Jesús Carranza Vega, Blanca Dina Valenzuela Robles, Erik Rosado Tamariz, and Andrés Blanco Ortega. 2026. "Comparative Analysis of Techniques for Texture Feature Extraction for Supervised Classification of Wood and Textile Waste" Recycling 11, no. 5: 86. https://doi.org/10.3390/recycling11050086

APA Style

Francisco, W. C., Tavira, J. V., Vega, J. J. C., Robles, B. D. V., Tamariz, E. R., & Ortega, A. B. (2026). Comparative Analysis of Techniques for Texture Feature Extraction for Supervised Classification of Wood and Textile Waste. Recycling, 11(5), 86. https://doi.org/10.3390/recycling11050086

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