FUZZY LOGIC MODELING OF THE OCULAR TEMPERATURE OF CATTLE IN THERMAL STRESS CONDITIONS

Lins, Ana C. de S. S.; Souza, Ingrid J. S.; Lourençoni, Dian; Yanagi Júnior, Tadayuki; Santos, Italo E. dos A.

doi:10.1590/1809-4430-Eng.Agric.v41n4p418-426/2021

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Scientific Papers, Agricultural Building and Environment • Eng. agríc. 41 (4) • Jul-Aug 2021 • https://doi.org/10.1590/1809-4430-Eng.Agric.v41n4p418-426/2021 copy

FUZZY LOGIC MODELING OF THE OCULAR TEMPERATURE OF CATTLE IN THERMAL STRESS CONDITIONS

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

This study proposes a fuzzy logic model capable of predicting the ocular temperature (OT) of beef cattle by means of infrared thermography. The goal of this study is to establish a methodology for making decisions related to animal welfare. The experiment was carried out at a commercial beef production farm, located in the south of Minas Gerais state, where twenty-eight Brahman cattle (Bos Taurus Indicus) raised in extensive production systems were evaluated. Thermal images of the entire head of the animal were collected in order to measure the ocular temperature (OT). Concurrently, the variables air dry bulb temperature (DBT) and relative humidity (RH) were recorded. The fuzzy logic model was developed using the Mandani inference method, based on the input variables DBT and RH and the output variable OT, and using the experimental data as reference. The proposed fuzzy logic system allows the estimation of the ocular temperature of beef cattle with an error of 1.71% and a coefficient of determination R ² of 0.8749. These values validate the proposed fuzzy logic system for helping to make decisions for better animal welfare.

thermal comfort; thermal imaging; beef cattle

Rule	Input variables		Output variable

	dry-bulb temperature (DBT)	relative humidity (RH)	Ocular temperature (ºC)
1	Cold	Low	Low
2	Cold	Comfort	Low
3	Cold	High	Comfort
4	Hot	Low	High
5	Hot	Comfort	High
6	Hot	High	High
7	Comfort	Low	Comfort
8	Comfort	Comfort	Comfort
9	Comfort	High	Comfort

Animal number	Input variables		Output variable

	Dry-bulb temperature (°C)	Relative Humidity (%)	Ocular temperature (°C)
1	32.3	50.4	30.3 (1.17)
2	33.5	53.7	30.9 (1.57)
3	33.8	49.9	31.4 (1.24)
4	32.5	48.1	30.1 (1.12)
5	32.6	49.2	30.3 (1.52)
6	33.3	49.0	31.1 (1.35)
7	33.0	49.0	30.5 (1.56)
8	32.9	48.2	30.6 (1.28)
9	33.1	45.9	31.0 (1.30)
10	33.6	45.0	31.6 (1.11)
11	34.1	42.8	32.1 (0.95)
12	34.6	40.4	31.8 (1.19)
13	35.1	35.0	32.6 (1.06)
14	35.5	34.1	33.8 (0.90)
15	35.9	33.2	33.4 (1.03)
16	36.1	31.3	33.9 (1.09)
17	36.9	27.8	35.3 (0.87)
18	37.1	28.3	35.6 (0.89)
19	37.3	28.0	35.7 (0.89)
20	37.3	26.5	35.8 (0.92)
21	37.4	24.1	35.8 (0.93)
22	36.5	23.9	35.0 (0.87)
23	37.3	23.6	36.1 (0.66)
24	38.1	23.7	36.3 (0.95)
25	39.7	23.8	38.5 (0.66)
26	40.1	24.2	38.3 (0.74)
27	40.3	25.4	38.8 (0.77)
28	40.4	24.2	39.1 (0.93)

Animal number	Experimental data	Fuzzy simulation	Standard deviation	Percentage error (%)
1	32.3	32.0	0.1	0.6
2	33.5	32.5	0.7	3.1
3	33.8	32.7	0.7	3.0
4	32.5	32.7	0.1	0.6
5	32.6	32.7	0.1	0.3
6	33.3	32.9	0.3	1.2
7	33.0	32.9	0.1	0.4
8	32.9	33.2	0.2	0.9
9	33.1	33.5	0.3	1.5
10	33.6	33.8	0.2	0.7
11	34.1	34.1	0.0	0.0
12	34.6	34.4	0.2	0.7
13	35.1	34.8	0.2	0.8
14	35.5	35.2	0.2	0.8
15	35.9	35.6	0.2	0.7
16	36.1	36.1	0.1	0.2
17	36.9	37.6	0.5	1.9
18	37.1	37.0	0.1	0.3
19	37.3	37.6	0.2	0.9
20	37.3	37.6	0.2	0.8
21	37.4	38.2	0.6	2.3
22	36.5	38.2	1.2	4.7
23	37.3	38.2	0.6	2.5
24	38.1	38.2	0.1	0.3
25	39.7	38.2	1.0	3.7
26	40.1	38.2	1.3	4.7
27	40.3	38.2	1.5	5.2
28	40.4	38.2	1.5	5.3

		Mean	0.5	1.71

Associação Brasileira de Engenharia Agrícola SBEA - Associação Brasileira de Engenharia Agrícola, Departamento de Engenharia e Ciências Exatas FCAV/UNESP, Prof. Paulo Donato Castellane, km 5, 14884.900 | Jaboticabal - SP, Tel./Fax: +55 16 3209 7619 - Jaboticabal - SP - Brazil
E-mail: revistasbea@sbea.org.br

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[1] 1* Corresponding author. Universidade Federal do Vale do São Francisco/ Juazeiro - BA, Brasil. E-mail: ana_carolina_lins@hotmail.com