CattleNet-XAI: An explainable CNN framework for efficient cattle weight estimation
Md Junayed Hossain, Jannatul Ferdaus, Ashraful Islam, M. Ashraful Amin, Agbotiname Lucky Imoize, Agbotiname Lucky Imoize, Agbotiname Lucky Imoize, Agbotiname Lucky Imoize, Agbotiname Lucky Imoize
TL;DR
This paper introduces CattleNet-XAI, an explainable CNN framework that improves the accuracy of cattle weight estimation using computer vision.
Contribution
The novel contribution is a custom CNN model (3Conv3Dense) that outperforms traditional and other CNN models in cattle weight prediction.
Findings
The 3Conv3Dense model achieved a MAE of 18.02 kg and RMSE of 19.85 kg, showing superior accuracy.
LIME visualization and error analysis were used to explain the model's decision-making process.
Advanced preprocessing techniques improved input data quality for better model performance.
Abstract
Accurate estimation of cattle weight is essential for effective farm management, health assessment, and productivity optimization. Traditional manual methods for weight estimation, however, are labor-intensive, time-consuming, and prone to inaccuracies. Recent advances in computer vision have facilitated the automation of weight prediction from image data. However, traditional regression models, such as Random Forest and Linear Regression, face challenges in capturing the complex, nonlinear relationships within image data, leading to less accurate predictions. To address these issues, we introduce CattleNet-XAI, a framework designed for both efficiency and explainability, which utilizes a custom Convolutional Neural Network (CNN). For the CNN-based approach, we incorporated advanced image preprocessing techniques, including normalization and histogram equalization, to enhance the input…
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Taxonomy
TopicsAnimal Behavior and Welfare Studies · Effects of Environmental Stressors on Livestock · Food Supply Chain Traceability