Cattle welfare assessment based on adaptive fuzzy logic and multimodal data fusion.

This study proposes a cattle welfare evaluation method based on multi-modal data fusion, which integrates various data dimensions, such as cattle behavior characteristics, feeding management conditions, and environmental parameters, to achieve a systematic assessment of cattle welfare levels. The method establishes a quantitative scoring system based on behavioral duration and individual group differences, and designs a multi-modal data processing framework that combines Backpropagation (BP) neural networks with adaptive fuzzy logic. This framework uses a Gaussian membership function to replace the traditional triangular membership function for feature mapping, significantly improving the robustness and accuracy of the evaluation system through a differentiated weight allocation strategy. By introducing a dynamic adaptive scoring mechanism, the model can automatically adjust evaluation parameters according to the actual application scenario, ensuring the practicality and reliability of the evaluation results. Experimental validation shows that the method performs excellently across the three evaluation dimensions of environment, feeding, and behavior: the environment evaluation module achieves accuracy rates of 88.7% and 95.0% for the training and validation sets, respectively; the feeding evaluation module achieves 98.3% and 100%, respectively; and the behavior evaluation module achieves 85.7% and 93.6%. The validation accuracy for all dimensions exceeds 90%. This method integrates multi-modal data, providing a reliable decision support tool for modern farms. It demonstrates strong adaptability and can be adjusted to suit different environments. The research results are of significant importance for promoting the intelligent transformation of farm management, contributing to enhancing operational efficiency and sustainability in farms of varying types and scales.