Decoding cow behavior patterns from accelerometer data using deep learning

Abstract

This article explores the novel application of deep learning methods in the analysis of complex cattle behavior patterns using accelerometer data. With the information provided by accelerometer data regarding the movements of cows, valuable insights into their health, behavior, and overall welfare can be understood. Manual deciphering of these patterns presents an overwhelming challenge owing to the intricate and fluctuating nature of cattle behavior. The principal objective of this research is to construct a deep learning framework that can precisely interpret complex cow behavior patterns and enable more precise and efficient surveillance. To achieve this objective, the input accelerometer data collected during various cattle behavioral instances, such as grazing, lying, walking, and other activities, undergo preprocessing and augmentation. The preprocessed data then undergo a deep learning framework comprised of 23 layers, incorporating convolution layers, batch normalization, rectified linear unit (ReLu), and MaxPooling layers. The model demonstrates promising performance in categorizing cow behaviors based on the unique movement signatures captured by the sensors. Through rigorous evaluation using three distinct datasets, each containing a different number of activities, we achieve high accuracy rates of 96.72%, 87.15%, and 98.7%, respectively. It enhances livestock management by automating behavior analysis, enabling real-time monitoring, and informed decision-making. Improved animal welfare is achieved through early detection of stress or illness, leading to prompt interventions.