An Explainable AI based approach for Monitoring Animal Health

TL;DR

This paper presents an explainable machine learning approach using accelerometer data and IoT devices to monitor cattle health, providing transparent insights for sustainable livestock management.

Contribution

It introduces a novel data-driven, explainable AI framework for cattle activity classification using accelerometer data and IoT, emphasizing model transparency and practical application.

Findings

K-nearest neighbor classifier achieved an AUC of 0.98 on training data.

Explainability frameworks like SHAP identified key features influencing model decisions.

The approach enables real-time, interpretable monitoring of cattle health.

Abstract

Monitoring cattle health and optimizing yield are key challenges faced by dairy farmers due to difficulties in tracking all animals on the farm. This work aims to showcase modern data-driven farming practices based on explainable machine learning(ML) methods that explain the activity and behaviour of dairy cattle (cows). Continuous data collection of 3-axis accelerometer sensors and usage of robust ML methodologies and algorithms, provide farmers and researchers with actionable information on cattle activity, allowing farmers to make informed decisions and incorporate sustainable practices. This study utilizes Bluetooth-based Internet of Things (IoT) devices and 4G networks for seamless data transmission, immediate analysis, inference generation, and explains the models performance with explainability frameworks. Special emphasis is put on the pre-processing of the accelerometers time series data, including the extraction of statistical characteristics, signal processing techniques, and lag-based features using the sliding window technique. Various hyperparameter-optimized ML models are evaluated across varying window lengths for activity classification. The k-nearest neighbour Classifier achieved the best performance, with AUC of mean 0.98 and standard deviation of 0.0026 on the training set and 0.99 on testing set). In order to ensure transparency, Explainable AI based frameworks such as SHAP is used to interpret feature importance that can be understood and used by practitioners. A detailed comparison of the important features, along with the stability analysis of selected features, supports development of explainable and practical ML models for sustainable livestock management.