Development of sound-based poultry health monitoring tool for automated sneeze detection

• Published in: Computers and electronics in agriculture Vol. 162; pp. 573 - 581
• Main Authors: Carpentier, Lenn, Vranken, Erik, Berckmans, Daniel, Paeshuyse, Jan, Norton, Tomas
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.07.2019; Elsevier BV
• Subjects: Acoustics; Algorithms; Automation; Bioacoustics; Broiler; Chickens; Health; Meat; Poultry; Precision livestock farming; Respiratory diseases; Sneezing; Sound; Sound analysis; Sound recordings; Subtraction; Bioacoustics; Precision livestock farming; Sound analysis; Respiratory diseases; Broiler
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2019.05.013

•Monitor bioacoustics to detect respiratory problems.•Algorithm to detect sneezing in broiler chickens.•Classification of unbalanced dataset.•The algorithm obtained a sensitivity of 66.7% and a precision of 88.4%. Respiratory diseases are a major health challenge in meat chicken production. As sneezing is a clinical sign of many respiratory diseases, sound has a great potential in monitoring these diseases. This study focussed on the development of an algorithm to monitor chicken sneezing sounds in a situation where multiple birds are active and multiple noise sources are present. An experiment was designed where the sneezing from within a group of 51 chickens was recorded. 763 sneezes were annotated out of 480 min of sound recordings. First, the number of labelled sneezes of adequate quality were investigated. Then raw sound signal was filtered using spectral subtraction and split into short intervals with elevated energy that could be sneezes. This led to a highly unbalanced dataset containing only 0.24% sneezes, from which features characterising the sneezing sounds were calculated. These were then grouped into 8 different features on which the algorithm classified the sound as sneeze or no-sneeze with a sensitivity of 66.7% and a precision of 88.4%. The algorithm enabled the monitoring of the number of sneezes in the experimental group. This work represents the first step towards the development of an automated sound-based monitoring system for poultry health.