Título:	Deep learning-based UAV framework for automated morphological and growth analysis of feedlot cattle.
Autor:	YAN, J. TETILA, E. C. ZHAO, L. GONÇALVES, R. C. CASTANHEIRO, L. F. VALEM, L. P. BARBEDO, J. G. A.
Afiliación:	JIANGLONG YAN, UNIVERSIDADE DE SÃO PAULO; EVERTON CASTELÃO TETILA, UNIVERSIDADE FEDERAL DA GRANDE DOURADOS; LIANG ZHAO, UNIVERSIDADE DE SÃO PAULO; RIAN C. GONÇALVES, UNIVERSIDADE FEDERAL DA GRANDE DOURADOS; LETÍCIA F. CASTANHEIRO; LUCAS P. VALEM, UNIVERSIDADE DE SÃO PAULO; JAYME GARCIA ARNAL BARBEDO, CNPTIA.
Año:	2026
Referencia:	Computers and Electronics in Agriculture, v. 245, 111559, Apr. 2026.
Descripción:	Accurate and continuous monitoring of cattle development is essential to optimize feedlot management and determine the most profitable slaughter period. Traditional weighing procedures are labor-intensive, intrusive, and difficult to scale, highlighting the need for automated, non-invasive longitudinal monitoring frameworks. In this study, we propose a UAV-based computer vision framework that integrates high-resolution aerial imagery with deep learning techniques to track the morphological development of beef cattle throughout the production cycle, rather than directly estimating body weight. A subset of images was annotated to fine-tune YOLOv11s for cattle detection, while instance-level body contours were extracted using SAM 2.1 without additional training. An automated filtering strategy retained only well-posed standing animals, enabling consistent extraction of morphological features such as body length, width, and Width-to-Length ratio (W/L), a scale-invariant proxy of body condition over time. Longitudinal, population-level analysis of Nellore cattle monitored over 112 days revealed a clear sigmoidal growth pattern, well described by a logistic model, and the growth curve uncovered three biological phases: lag, rapid development, and plateau, allowing the framework to qualitatively determine an appropriate economic marketing window. These results demonstrate the practical value of the proposed framework for scalable, data-driven precision livestock management in commercial feedlot environment.
NAL Thesaurus:	Computer vision Beef cattle
Palabras clave:	Pecuária de precisão Pecuária de corte Gado de corte confinado Monitoramento de crescimento de gado Modelagem de crescimento de gado Extração de características morfológicas Seleção automática de gado Visão computacional Aprendizado profundo Deep learning UAV imagery Cattle growth monitoring and modeling Morphological feature extraction Automatic cattle selection Precision
DOI:	https://doi.org/10.1016/j.compag.2026.111559
Tipo de Material:	Artigo de periódico
Acceso:	openAccess
Aparece en las colecciones:	Artigo em periódico indexado (CNPTIA)