Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior

TL;DR

This paper discusses the challenges and lessons learned in applying time series classification to large-scale chicken behavior data, emphasizing the importance of effective data pre-processing for improving animal welfare insights.

Contribution

It provides practical insights into pre-processing techniques for big behavioral data and highlights their significance in the context of poultry welfare monitoring.

Findings

Pre-processing is crucial for successful behavioral data analysis.

Handling big data in poultry studies presents unique challenges.

Effective data cleansing improves classification accuracy.

Abstract

Poultry farms are a major contributor to the human food chain. However, around the world, there have been growing concerns about the quality of life for the livestock in poultry farms; and increasingly vocal demands for improved standards of animal welfare. Recent advances in sensing technologies and machine learning allow the possibility of monitoring birds, and employing the lessons learned to improve the welfare for all birds. This task superficially appears to be easy, yet, studying behavioral patterns involves collecting enormous amounts of data, justifying the term Big Data. Before the big data can be used for analytical purposes to tease out meaningful, well-conserved behavioral patterns, the collected data needs to be pre-processed. The pre-processing refers to processes for cleansing and preparing data so that it is in the format ready to be analyzed by downstream algorithms, such as classification and clustering algorithms. However, as we shall demonstrate, efficient pre-processing of chicken big data is both non-trivial and crucial towards success of further analytics.