Predicting carcass tissue composition in Blackbelly sheep using ultrasound measurements and machine learning methods.

Autor: Camacho-Pérez E; Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes S/N, Mérida, Yucatán, México., Lugo-Quintal JM; Tecnológico Nacional de México, Instituto Tecnológico Superior Progreso, Progreso, Yucatán, México., Tirink C; Faculty of Agriculture, Department of Animal Science, Igdir University, TR76000, Igdir, Turkey., Aguilar-Quiñonez JA; Facultad de Agronomía, Universidad Autónoma de Sinaloa, Km 17.5 Carretera Culiacán-El Dorado, Culiacán, 80000, Sinaloa, México., Gastelum-Delgado MA; Facultad de Agronomía, Universidad Autónoma de Sinaloa, Km 17.5 Carretera Culiacán-El Dorado, Culiacán, 80000, Sinaloa, México., Lee-Rangel HA; Centro de Biociencias, Facultad de Agronomía y Veterinaria, Instituto de Investigaciones en Zonas Desérticas, Universidad Autónoma de San Luis Potosí, Km 14.5 Carr, San Luis Potosí-Matehuala, 78321, México., Roque-Jiménez JA; Centro de Biociencias, Facultad de Agronomía y Veterinaria, Instituto de Investigaciones en Zonas Desérticas, Universidad Autónoma de San Luis Potosí, Km 14.5 Carr, San Luis Potosí-Matehuala, 78321, México., Garcia-Herrera RA; División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carr. Villahermosa-Teapa, Km 25, CP 86280, Villahermosa, Tabasco, México., Chay-Canul AJ; División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carr. Villahermosa-Teapa, Km 25, CP 86280, Villahermosa, Tabasco, México. alfonso.chay@ujat.mx.
Jazyk: angličtina
Zdroj: Tropical animal health and production [Trop Anim Health Prod] 2023 Sep 19; Vol. 55 (5), pp. 300. Date of Electronic Publication: 2023 Sep 19.
DOI: 10.1007/s11250-023-03759-1
Abstrakt: This study aimed to predict Blackbelly sheep carcass tissue composition using ultrasound measurements and machine learning models. The models evaluated were decision trees, random forests, support vector machines, and multi-layer perceptrons and were used to predict the total carcass bone (TCB), total carcass fat (TCF), and total carcass muscle (TCM). The best model for predicting the three parameters, TCB, TCF, and TCM was random forests, with mean squared error (MSE) of 0.31, 0.33, and 0.53; mean absolute error (MAE) of 0.26, 0.29, and 0.53; and the coefficient of determination (R 2 ) of 0.67, 0.69, and 0.76, respectively. The results showed that machine learning methods from in vivo ultrasound measurements can be used as determinants of carcass tissue composition, resulting in reliable results.
(© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)
Databáze: MEDLINE
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