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LIVESTOCK WEIGHT ESTIMATION USING MACHINE LEARNING TECHNIQUES AND REGRESSION METHODS
Dandu Satyanuarga; Dr. Ir. Rila Mandala, M. Eng.; Nugraha Priya Utama, S.T., M.A., Ph.D.

School of Electrical Engineering and Informatics,
Bandung Institute of Technology
Jl. Ganesha No.10, Lb. Siliwangi, Coblong, Bandung, Jawa Barat 40132, Indonesia


Abstract

The application of image processing to measure the weight of pigs has been widely applied to simplify the process of animal care. This method utilizes image segmentation and statistical models. Many parameters are restrictions that make it difficult to efficiently use the previous method. The research carried out wants to cut the boundaries in taking pictures and apply them to cows and sheep. The purpose of this study is to develop technology in the industry in Indonesia, especially in factory farming. This study wants to simplify the measurement of animal weight by using machine learning models and regression methods. The existence of a model that can automatically simplify the work of farmers to care for animals or facilitate traders to determine the selling price of livestock. Mask R-CNN not only detect animal labels but also can detect and mark every pixel on the detection object. Regression models produce estimates of animal weight in the figure. The regression model used is the K-Nearest Neighbor regression model by utilizing the K-Means model to create clusters in each type of animal. The R-CNN mask model tested in this study can detect cattle and sheep in data taken with 100% accuracy. The regression model used in this study can reach an accuracy value of 98% for cattle and sheep. The weight prediction results obtained are very accurate with an error value in the form of an estimated difference with the original weight of less than 10%.

Keywords: Mask R-CNN, Regression, Estimate weight, Cow, Sheep

Topic: Biomedical, Robotic and ICT engineering

Link: https://ifory.id/abstract/HZWbtJPeuULA

Conference: The 1st Bandung Applied Biomedical and Technology in Health Conference (BABTECH 2019)

Plain Format | Corresponding Author (Dandu Satyanuraga)

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