Date of Publication

4-24-2023

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Electronics Engineering

Subject Categories

Electrical and Computer Engineering

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Thesis Advisor

Gerino P. Mappatao
Melchizedek I. Alipio

Defense Panel Chair

Leonard U. Ambata

Defense Panel Member

Ma. Antonette C. Roque
Melvin K. Cabatuan

Abstract/Summary

Goat farming shows potential as one of the solutions for providing quality livestock products in developing countries. However, as with humans, goats require good health to yield high-quality products. Through this study, the researchers created a wearable device consisting of an accelerometer, gyrometer, and temperature sensors to monitor the goat’s feeding behavior. The data collected by these sensors were transmitted to a spreadsheet file for processing in a MATLAB application. This application made use of the k-NN machine-learning algorithm for accurate prediction. Verifying the algorithm was done by comparing video footage with the predictions made by the algorithm. Lastly, a knowledge-building portal was created to relay important information concerning goats to livestock farmers. Results show that the goats felt comfortable wearing the device. The application also predicted the goat’s feeding pattern with an accuracy of 98.02% for the sensor data. Furthermore, the farmers showed interest in the knowledge-building portal and recommended it to other farmers. In improving the study, the researchers recommended using other machine learning algorithms for data classification.

Abstract Format

html

Language

English

Format

Electronic

Keywords

Goats—Monitoring; Goats—Feeding and feeds; Goats—Behavior; Goats—Technological innovations

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Embargo Period

4-23-2023

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