Development of Noncontact Thermometer based Artificial Neural Network for Cattle Health Monitoring

Mareli Telaumbanua; Agapetalia  Indriyawati; Agus Haryanto; Budianto  Lanya; Febryan Kusuma Wisnu; Winda Rahmawati

doi:10.22146/jnteti.v15i2.12176

Mareli Telaumbanua Department of Agricultural Engineering, Faculty of Agriculture, University of Lampung, Lampung 35145, Indonesia
Agapetalia Indriyawati Department of Biosystems Engineering, Faculty of Industrial Technology, Institut Teknologi Sumatera, Lampung 35365, Indonesia
Agus Haryanto Department of Agricultural Engineering, Faculty of Agriculture, University of Lampung, Lampung 35145, Indonesia
Budianto Lanya Department of Agricultural Engineering, Faculty of Agriculture, University of Lampung, Lampung 35145, Indonesia
Febryan Kusuma Wisnu Department of Agricultural Engineering, Faculty of Agriculture, University of Lampung, Lampung 35145, Indonesia
Winda Rahmawati Department of Agricultural Engineering, Faculty of Agriculture, University of Lampung, Lampung 35145, Indonesia

DOI: https://doi.org/10.22146/jnteti.v15i2.12176

Keywords: Artificial Neural Networks, Microcontroller, Temperature Sensor, Noncontact Thermometer, Ultrasonic Sensor

Abstract

Manual measurement of animal body temperature is done by inserting a thermometer into the rectum or anus. This causes discomfort, stress, and the risk of disease transmission. Therefore, a method of measuring body temperature with a noncontact thermometer is needed. Noncontact sensors for human skin have different emissivity levels than cowhide surfaces. This study aimed to design and test a noncontact temperature measuring instrument based on an artificial neural network (ANN) model for cows. Observation parameters include performance tests, coefficient of determination (R²), root mean square error (RMSE), and relative RMSE (RRMSE). The development process of the ANN used 2 input layers, 3 hidden layers 1, 2 hidden layers 2, and 1 output layer. The type of training used was trainlm, and a learning rate of 0.001, producing the best prediction with the activation function tansig-logsig-tagsig. The sensor design produced R², RMSE and RRMSE values that were in accordance with the research parameters, namely obtaining an R² value of 0.9909 or 99.09%, an RMSE value of 0.6361 and an RRMSE value of 0.13% on cowhide. The design of the sensor device tested on live cows produced an R² value of 0.6429 or 64.29%. The most accurate distance was in the range of 4 cm to 6 cm with an error value of 0.606. The noncontact thermometer is able to measure the body temperature of cows at close range with high accuracy.

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