Bull Breeding Soundness Assessment Using Artificial Neural Network-Based Predictive Models

Luis F. Marín-Urías, Pedro J. García-Ramírez (), Belisario Domínguez-Mancera, Antonio Hernández-Beltrán, José A. Vásquez-Santacruz, Patricia Cervantes-Acosta, Manuel Barrientos-Morales and Rogelio de J. Portillo-Vélez
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Luis F. Marín-Urías: School of Electrical and Electronic Engineering, Universidad Veracruzana, Boca del Río 94292, Mexico
Pedro J. García-Ramírez: Institute of Engineering, Universidad Veracruzana, Boca del Río 94292, Mexico
Belisario Domínguez-Mancera: School of Veterinary Medicine and Animal Science, Universidad Veracruzana, Veracruz 91897, Mexico
Antonio Hernández-Beltrán: School of Veterinary Medicine and Animal Science, Universidad Veracruzana, Veracruz 91897, Mexico
José A. Vásquez-Santacruz: School of Electrical and Electronic Engineering, Universidad Veracruzana, Boca del Río 94292, Mexico
Patricia Cervantes-Acosta: School of Veterinary Medicine and Animal Science, Universidad Veracruzana, Veracruz 91897, Mexico
Manuel Barrientos-Morales: School of Veterinary Medicine and Animal Science, Universidad Veracruzana, Veracruz 91897, Mexico
Rogelio de J. Portillo-Vélez: School of Electrical and Electronic Engineering, Universidad Veracruzana, Boca del Río 94292, Mexico

Agriculture, 2023, vol. 14, issue 1, 1-16

Abstract: For years, efforts have been devoted to establishing an effective bull breeding soundness evaluation procedure; usual research on this subject is based on bull breeding soundness examination (BBSE) methodologies, which have significant limitations in terms of their evaluation procedure, such as their high cost, time consumption, and administrative difficulty, as well as a lack of diagnostic laboratories equipped to handle the more difficult cases. This research focused on the creation of a prediction model to supplement and/or improve the BBSE approach through the study of two algorithms, namely, clustering and artificial neural networks (ANNs), to find the optimum machine learning (ML) approach for our application, with an emphasis on data categorization accuracy. This tool was designed to assist veterinary medicine and farmers in identifying key factors and increasing certainty in their decision-making during the selection of bulls for breeding purposes, providing data from a limited number of factors generated from a deep pairing study of bulls. Zebu, European, and crossbred bulls were the general groupings. The data utilized in the model’s creation (N = 359) considered five variables that influence improvement decisions. This approach enhanced decision-making by 12% compared to traditional breeding bull management. ANN obtained an accuracy of 90%, with precision rates of 97% for satisfactory, 92% for unsatisfactory, and 85% for bad. These results indicate that the proposed method can be considered an effective alternative for innovative decision-making in traditional BBSE.

Keywords: BBSE; artificial neural networks; K-means; data engineering (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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