Classification of Body Weight in Beef Cattle via Machine Learning Methods: A Review

Hakem, Moad; Boulouard, Zakaria; Kissi, Mohamed

doi:10.1016/j.procs.2021.12.238

Cited by 10 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SVM aims to find a linear separating hyperplane maximizing the distance to the nearest individuals of each of the two classes, called margins [2]. SVM can be used as a classification or regression algorithm [36,37]. The original idea of the SVM method is to issue two classes, one above the first class vector and the other below the second class vector [38].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

See 1 more Smart Citation

Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology

2022

View full text Add to dashboard Cite

Sex classification is part of forensic anthropological identification aimed at determining whether the skeleton belongs to a male or a female. This paper exhibits the performance of the Support Vector Machine (SVM) in classifying the sex of the sacrum in forensic anthropology. Bone data was measured by the metric method based on six variables, namely superior breadth, anterior length, mid ventral breadth, real height, diameter the base, and max-transverse diameter of the base. This study shows performance analysis of SVM using the library libSVM with linear, polynomial, and RBF kernel to observe the results of the comparison of the accuracy of the kernel used. According to the results of the trials, the best accuracy was attained in each kernel function, i.e., the RBF kernel is 83.33% with g = 1 and C = 1, the polynomial is 85.56% at γ = 2, C = 2 and d =1, and the linear kernel obtained best accuracy is 84.44 % with C = 2 and C = 3. In conformity with the experimental result, polynomial attained the highest accuracy of 85.56% at γ = 2, C = 2, and d =1.

show abstract

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…SVM algorithms have a good application to issue two classes and provide excellent classification performance [32,39]. SVM constructs a set of hyperplanes that separates data into categories [36]. SVM issues a hyperplane (linear boundary) for the two data classes [40].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology

2022

View full text Add to dashboard Cite

show abstract

“…Random forest is a popular ML algorithm that is widely used in classification and regression and has various advantages and high accuracy [ 15 ]. Therefore, this study aimed to predict the BW of a BB X FH crossbred in Indonesia based on morphometrics using random forest.…”

Section: Introductionmentioning

confidence: 99%

Body weight prediction of Belgian Blue crossbred using random forest

Praharani,

Talib,

Kusumaningrum

et al. 2024

J Adv Vet Anim Res

View full text Add to dashboard Cite

Objective: The aim of this study was to predict the body weight (BW) of a Belgian Blue X Friesian Holstein (BB X FH) crossbred in Indonesia based on morphometrics using random forest. Materials and Methods: A total of 26 BB X FH crossbreds were observed for BW, chest weight (CW), body length (BL), hip height (HH), wither height (WH), and chest girth (CG) from 0, 30, 60, 90, 120, 150, 180, 210, 240, 270, and 300 days of age. Stepwise regression and random forest were performed using R 3.6.1. Results: The random forest results show that CG is an important variable in estimating BW, with an important variable value of 24.49%. Likewise, the results obtained by stepwise regression show that CG can be an indicator of selection for the BB X FH crossbred. The R squared value obtained from the regression is 0.83, while the R squared value obtained from the random forest (0.86) is greater than the regression. Conclusion: In conclusion, random forest produces a better model than stepwise regression. However, a good simple equation to use to estimate BW is CG.

show abstract

“…For this purpose, besides the reference cards and image processing software (Ozkaya and Bozkurt, 2008 ; Weber et al, 2020a ), different computer vision methods have been attempted to calculate the body areas and height including the Euclidean distances (Weber et al, 2020b ), EfficientNet, ResNet, Recurrent Attention Model (Gjergji et al, 2020 ). Moreover, considering the strong correlation between the body parameters from the images and cattle weight, the regression-based machine learning methods, for instance, multiple linear regression (MLR) (Freund et al, 2006 ), support vector machine (SVM) (Boser et al, 1992 ), backpropagation (BP) neural network (Hakem et al, 2022 ) were used to predict the body weight.…”

Section: Introductionmentioning

confidence: 99%

Intelligent weight prediction of cows based on semantic segmentation and back propagation neural network

Xu,

Mao,

Wang

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

Accurate prediction of cattle weight is essential for enhancing the efficiency and sustainability of livestock management practices. However, conventional methods often involve labor-intensive procedures and lack instant and non-invasive solutions. This study proposed an intelligent weight prediction approach for cows based on semantic segmentation and Back Propagation (BP) neural network. The proposed semantic segmentation method leveraged a hybrid model which combined ResNet-101-D with the Squeeze-and-Excitation (SE) attention mechanism to obtain precise morphological features from cow images. The body size parameters and physical measurements were then used for training the regression-based machine learning models to estimate the weight of individual cattle. The comparative analysis methods revealed that the BP neural network achieved the best results with an MAE of 13.11 pounds and an RMSE of 22.73 pounds. By eliminating the need for physical contact, this approach not only improves animal welfare but also mitigates potential risks. The work addresses the specific needs of welfare farming and aims to promote animal welfare and advance the field of precision agriculture.

show abstract

Classification of Body Weight in Beef Cattle via Machine Learning Methods: A Review

Cited by 10 publications

References 4 publications

Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology

Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology

Body weight prediction of Belgian Blue crossbred using random forest

Intelligent weight prediction of cows based on semantic segmentation and back propagation neural network

Contact Info

Product

Resources

About