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CattleNet-XAI: An explainable CNN framework for efficient cattle weight estimation.

Md Junayed Hossain¹, Jannatul Ferdaus¹, Ashraful Islam¹

¹Center for Computational & Data Sciences, Independent University, Bangladesh, Dhaka, Bangladesh.

|November 13, 2025

Summary

Accurate cattle weight estimation is automated using a custom Convolutional Neural Network (CNN) model, CattleNet-XAI. This deep learning approach significantly improves prediction accuracy over traditional methods for better livestock management.

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Area of Science:

Agricultural Science
Computer Science
Machine Learning

Background:

Manual cattle weight estimation is inaccurate and labor-intensive.
Traditional regression models struggle with complex image data for weight prediction.
Automated methods are needed for efficient and precise livestock management.

Purpose of the Study:

To develop an efficient and explainable framework (CattleNet-XAI) for automated cattle weight estimation.
To compare the performance of a custom Convolutional Neural Network (CNN) against other models.
To enhance the accuracy of weight prediction using advanced image processing and deep learning.

Main Methods:

Developed CattleNet-XAI, a custom CNN framework with advanced image preprocessing.
Utilized YOLOv5 for feature extraction in traditional machine learning models.
Trained and evaluated multiple models including CNNs, EfficientNetB3, Random Forest, and Linear Regression.
Measured performance using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).

Main Results:

The custom CNN model (3Conv3Dense variation) achieved superior accuracy.
Achieved a Mean Absolute Error (MAE) of 18.02 kg and Root Mean Squared Error (RMSE) of 19.85 kg.
Demonstrated significant improvement over traditional machine learning and other CNN models.

Conclusions:

Deep learning, particularly CNNs, offers a highly accurate and automated solution for livestock weight estimation.
CattleNet-XAI provides an effective and explainable approach to modern cattle management.
Automated weight estimation enhances farm management, health assessment, and productivity optimization.