Cattle Weight Estimation Using Linear Regression and Random Forest Regressor

Cattle Weight Estimation Using Linear Regression and Random Forest Regressor

cattle; machine learning; linear regression; random forest regressor; prediction model

The global cattle farming industry has benefits as a food source, livelihood, economic contribution, land environmental restoration, and energy source. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. The authors propose estimating cattle weighting linear regression and random forest regression. Linear regression can interpret the linear relationship between dependent and independent variables, and random forest regression can generalize the data well. The dataset used in this study consisted of ten variables: live body weight, withers height, sacrum height, chest depth, chest width, maclocks width, hip joint width, oblique body length, oblique back length, and chest circumference. To find out the model that produces the smallest MAE value. The results show that the linear regression algorithm can produce estimated weight values for cattle with the best performance. This model produces a mean absolute error (MAE) of 0.35 kg, a mean absolute percentage error (MAPE) of 0.07%, a root mean square error (RMSE) of 0.5 kg, and an R² of 0.99. Each variable has excellent correlation performance results and contributes to computer vision and machine learning

Anjar Setiawan1, Ema Utami2, Dhani Ariatmanto3

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5494/894

Magister of Informatics Engineering, Universitas AMIKOM Yogyakarta, Yogyakarta, Indonesia

10-02-2024

FAJAR BAGUS W

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