This figure illustrates the four distinct custom Convolutional Neural Network (CNN) architectures evaluated in this study.

<p>Each diagram showcases a unique configuration, highlighting the variations in the number of convolutional and dense layers designed to assess the impact of architectural depth on the model’s performance in predicting cattle weight.</p>

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Bibliographic Details
Main Author:	Md Junayed Hossain (22615268) (author)
Other Authors:	Jannatul Ferdaus (22140270) (author), Ashraful Islam (1283403) (author), M. Ashraful Amin (22615271) (author)
Published:	2025
Subjects:	Sociology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified precise feature extraction mean absolute error less accurate predictions error case analysis demonstrates superior accuracy input data quality effective farm management pretrained efficientnetb3 model model significantly surpasses traditional manual methods associated weight data livestock weight estimation explainable cnn framework traditional regression models traditional methods weight estimation image data linear regression framework designed effective approach weight prediction cattle weight yolov5 algorithm study emphasizes results show recent advances random forest provide insights productivity optimization one variation mse ), making process low mae including normalization histogram equalization health assessment first leveraged face challenges especially cnn deep learning custom cnn conventional models computer vision cnn models cnn ). cattle images based approach 85 kg 02 kg
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This figure illustrates the four distinct custom Convolutional Neural Network (CNN) architectures evaluated in this study.

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