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Evaluation of lameness detection using radar sensing in ruminants
Author(s) -
Busin Valentina,
Viora Lorenzo,
King George,
Tomlinson Martin,
LeKernec Julien,
Jonsson Nicholas,
Fioranelli Francesco
Publication year - 2019
Publication title -
veterinary record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.261
H-Index - 99
eISSN - 2042-7670
pISSN - 0042-4900
DOI - 10.1136/vr.105407
Subject(s) - lameness , radar , gold standard (test) , limiting , livestock , computer science , sensitivity (control systems) , artificial intelligence , veterinary medicine , medicine , mathematics , biology , statistics , engineering , telecommunications , radiology , ecology , mechanical engineering , electronic engineering
Background Lameness is a major health, welfare and production‐limiting condition for the livestock industries. The current ‘gold‐standard’ method of assessing lameness by visual locomotion scoring is subjective and time consuming, whereas recent technological advancements have enabled the development of alternative and more objective methods for its detection. Methods This study evaluated a novel lameness detection method using micro‐Doppler radar signatures to categorise animals as lame or non‐lame. Animals were visually scored by veterinarian and radar data were collected for the same animals. Results A machine learning algorithm was developed to interpret the radar signatures and provide automatic classification of the animals. Using veterinary scoring as a standard method, the classification by radar signature provided 85 per cent sensitivity and 81 per cent specificity for cattle and 96 per cent sensitivity and 94 per cent specificity for sheep. Conclusion This radar sensing method shows promise for the development of a highly functional, rapid and reliable recognition tool of lame animals, which could be integrated into automatic, on‐farm systems for sheep and cattle.