Can We Reliably Detect Respiratory Diseases through Precision Farming? A Systematic Review

• Published in: Animals (Basel) Vol. 13; no. 7; p. 1273
• Main Authors: Garrido, Luís F C, Sato, Sabrina T M, Costa, Leandro B, Daros, Ruan R
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.04.2023; MDPI
• Subjects: Agricultural industry; Agriculture; Animal diseases; animal welfare; Animals; Bias; bioacoustic; Cattle; Criteria; Development and progression; Disease detection; Efficiency; Farming; Farms; Field tests; health monitoring; Laboratories; Livestock; Livestock farming; Livestock farms; Lung diseases; Medical research; Medicine, Experimental; Monitoring; Poultry; Poultry industry; Precision farming; Respiratory diseases; sensor; Sensors; smart farming; Swine; Systematic Review; Technology; Technology assessment; bioacoustic; sensor; smart farming; animal welfare; health monitoring
• ISSN: 2076-2615; 2076-2615
• DOI: 10.3390/ani13071273

Respiratory diseases commonly affect livestock species, negatively impacting animal's productivity and welfare. The use of precision livestock farming (PLF) applied in respiratory disease detection has been developed for several species. The aim of this systematic review was to evaluate if PLF technologies can reliably monitor clinical signs or detect cases of respiratory diseases. A technology was considered reliable if high performance was achieved (sensitivity > 90% and specificity or precision > 90%) under field conditions and using a reliable reference test. Risk of bias was assessed, and only technologies tested in studies with low risk of bias were considered reliable. From 23 studies included-swine (13), poultry (6), and bovine (4) -only three complied with our reliability criteria; however, two of these were considered to have a high risk of bias. Thus, only one swine technology fully fit our criteria. Future studies should include field tests and use previously validated reference tests to assess technology's performance. In conclusion, relying completely on PLF for monitoring respiratory diseases is still a challenge, though several technologies are promising, having high performance in field tests.