Fig 1.
Proposed architecture of our research.
Fig 2.
A summary of the dataset containing information on each cattle’s weight, breed, and height.
Fig 3.
Height-Weight correlation and breed distribution of cattle in the dataset.
Fig 4.
Image processing steps applied in our research.
Fig 5.
Here is the extracted cattle’s image from the original image using YOLOv5 algorithm, which accurately detects and isolates the cattle for further processing.
Fig 6.
This figure illustrates the four distinct custom Convolutional Neural Network (CNN) architectures evaluated in this study.
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.
Fig 7.
Architecture of the EfficientNetB3-based regression model used for cattle weight prediction.
The pre-trained EfficientNetB3 model is used as a feature extractor, followed by global average pooling and fully connected layers for regression.
Table 1.
MAE, MAPE, MSE, RMSE, R2 and Training Time (ms) for different algorithms utilised in this research.
Fig 8.
Comparison of Actual vs. Predicted values across different models used in this study.
The graphs illustrate the performance of each model in predicting cattle weight and highlighting how closely the predicted values align with the actual weights.
Fig 9.
Visualization of Residuals across different models applied in our research.
The residual plots help assess model performance by highlighting deviations between actual and predicted weights.
Fig 10.
Validation Loss vs. Training Loss Curves for the CNN architectures and EfficientNet B3 model evaluated in this study.
These plots illustrate the learning behavior of each model during training, helping to assess convergence, overfitting, and generalization capability.
Fig 11.
Prediction percentage distribution using different algorithms applied in our research.
Fig 12.
Here are the samples prediction of cattle weight using our proposed best 3Conv3Dense model.
Table 2.
Performance comparison of our proposed model with existing approaches for cattle weight prediction using machine learning and deep learning techniques.
Fig 13.
LIME visualizations for the 3Conv3Dense model, providing insight into its prediction process.
The highlighted regions indicate the key visual features that contributed most to the weight estimation. The visualizations show the model consistently focusing on anatomically relevant areas, such as the rib cage and abdomen.
Fig 14.
Error case analysis using LIME visualization on cattle’s weight prediction.