Prediction of thermal comfort level for dairy cattle: method based on machine learning modelling using climate chamber experiment data

• Grant number: 19/26828-1
• Support Opportunities: Scholarships in Brazil - Master
• Start date: July 01, 2020
• End date: January 31, 2022
• Field of knowledge: Agronomical Sciences - Agricultural Engineering
• Principal Investigator: Rafael Vieira de Sousa
• Grantee: Alex Vinicius da Silva Rodrigues
• Host Institution: Faculdade de Zootecnia e Engenharia de Alimentos (FZEA). Universidade de São Paulo (USP). Pirassununga , SP, Brazil

Abstract

Recent studies in animal production have investigated technologies and computational models for predicting the level of thermal comfort through noninvasive and automatic measurements. To contribute to this theme, the project aims to construct and test computational models to predict the thermal comfort level of dairy cattle using a database constructed by climate chamber experiment. The experiment will be conducted for 25 days with 12 Holstein heifers randomly assigned to two groups: stress group and control group. The animals in the stress group will be allocated to the climate chamber (in a Tie Stall system) and exposed to two heat waves. The control group will remain in individual pens partially covered in room temperature containment. During the period of the experiment, besides the meteorological data of the facilities, will be collected 5 times a day (6, 10, 14, 18 and 22 hours) data of rectal temperature, respiratory rate, sweating rate and body surface temperature in different areas of the body (eye, forehead, rib and flank) by infrared thermography. For the modeling step, different Machine Learning based algorithms (artificial neural networks, support vector machine, random forest and k-nearest neighbors) will be evaluated using different combinations of prediction inputs or thermal comfort level attribute classification. The tests to compare the prediction models will use linear regression (correlation coefficient and residual) and error (mean percentage error and mean square error) as an analysis metric. To compare the classification models will be used as metric the parameters obtained from the confusion matrix generated (accuracy, precision, sensitivity and F1 score). Through the project results we seek to highlight not only the best computational techniques for modelling, but also to highlight the potential of using a climate chamber experiment in order to create a more homogeneous and faster database in relation to techniques based on outdoor experiment. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

RODRIGUES, ALEX VINICIUS DA SILVA; MARTELLO, LUCIANE SILVA; PACHECO, VERONICA MADEIRA; SARDINHA, EDSON JOSE DE SOUZA; PEREIRA, ANDRE LEVI VIANA; DE SOUSA, RAFAEL VIEIRA. Thermal signature: A method to extract characteristics from infrared thermography data applied to the development of animal heat stress classifier models. Journal of Thermal Biology, v. 115, p. 8-pg., 2023-07-01. (19/26828-1, 18/05989-4)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

RODRIGUES, Alex Vinicius da Silva. Non-invasive assessment of heat stress in cattle: an approach based on machine learning and infrared thermography. 2022. Master's Dissertation - Universidade de São Paulo (USP). Faculdade de Zootecnica e Engenharia de Alimentos (FZE/BT) Pirassununga.