A study on the use of thermal imaging as a diagnostic tool for the detection of digital dermatitis in dairy cattle

Anagnostopoulos, Alkiviadis; Barden, Matthew; Tulloch, John; Williams, Karen; Griffiths, Bethany E; Bedford, C; Rudd, M; Psifidi, Androniki; Banos, Georgios; Oikonomou, Georgios

doi:10.3168/jds.2021-20178

Cited by 15 publications

(13 citation statements)

References 21 publications

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“…Emissivity values were reported in 64 studies (58.72%) ( 8 – 71 ) with 45 studies ( 72 – 116 ) not stating the emissivity value used. Figure 4 shows the range of emissivity values used for each species in the review, ranging from 0.93 to 1.0.…”

Section: Resultsmentioning

confidence: 99%

Thermography for disease detection in livestock: A scoping review

et al. 2022

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Infra-red thermography (IRT) offers potential opportunities as a tool for disease detection in livestock. Despite considerable research in this area, there are no common standards or protocols for managing IRT parameters in animal disease detection research. In this review, we investigate parameters that are essential to the progression of this tool and make recommendations for their use based on the literature found and the veterinary thermography guidelines from the American Academy of Thermology. We analyzed a defined set of 109 articles concerned with the use of IRT in livestock related to disease and from these articles, parameters for accurate IRT were identified and sorted into the fields of camera-, animal- or environment-related categories to assess the practices of each article in reporting parameters. This review demonstrates the inconsistencies in practice across peer-reviewed articles and reveals that some important parameters are completely unreported while others are incorrectly captured and/or under-represented in the literature. Further to this, our review highlights the lack of measured emissivity values for live animals in multiple species. We present guidelines for the standards of parameters that should be used and reported in future experiments and discuss potential opportunities and challenges associated with using IRT for disease detection in livestock.

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Section: Resultsmentioning

confidence: 99%

Thermography for disease detection in livestock: A scoping review

et al. 2022

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“…When looking at feet with any DD lesions, there was no association with the dichotomized IRTmax. Previous work reported poor test characteristics to diagnose the presence of DD lesions using IRTmax with Se 0.75–0.89 and Sp 0.65–0.70 [ 13 , 29 ]. Altogether, these findings suggest that it is unlikely that a cut-off value for IRTmax with high Se and Sp for the detection of feet with M2 lesions can be determined using cross-sectional data.…”

Section: Discussionmentioning

confidence: 99%

Broad-spectrum infrared thermography for detection of M2 digital dermatitis lesions on hind feet of standing dairy cattle

et al. 2023

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Low-effort, reliable diagnostics of digital dermatitis (DD) are needed, especially for lesions warranting treatment, regardless of milking system or hygienic condition of the feet. The primary aim of this study was to test the association of infrared thermography (IRT) from unwashed hind feet with painful M2 lesions under farm conditions, with lesion detection as ultimate goal. Secondary objectives were to determine the association between IRT from washed feet and M2 lesions, and between IRT from unwashed and washed feet and the presence of any DD lesion. A total of 641 hind feet were given an M-score and IRT images of the plantar pastern were captured. Multivariable logistic regression analyses were done with DD status as dependent variable and maximum infrared temperature (IRTmax), lower leg cleanliness score and locomotion score as independent variables, and farm as fixed effect. To further our understanding of IRTmax within DD status, we divided IRTmax into two groups over the median value of IRTmax in the datasets of unwashed and washed feet, respectively, and repeated the multivariable logistic regression analyses. Higher IRTmax from unwashed hind feet were associated with M2 lesions or DD lesions, in comparison with feet without an M2 lesion or without DD, adjusted odds ratio 1.6 (95% CI 1.2–2.2) and 1.1 (95% CI 1.1–1.2), respectively. Washing of the feet resulted in similar associations. Dichotomization of IRTmax substantially enlarged the 95% CI for the association with feet with M2 lesions indicating that the association becomes less reliable. This makes it unlikely that IRTmax alone can be used for automated detection of feet with an M2 lesion. However, IRTmax can have a role in identifying feet at-risk for compromised foot health that need further examination, and could therefore function as a tool aiding in the automated monitoring of foot health on dairy herds.

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“…To address this issue, thermal imaging cow detection has been explored [2]. Thermal imaging uses infrared technology to detect the heat signature of an object, which allows for more accurate detection in low-light or low-contrast environments.…”

Section: Related Workmentioning

confidence: 99%

Dataset artificial augmentation with a small number of training samples for reflectance estimation

Zhang

Wang

2023

Opt. Express

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The accuracy of the spectral reflectance estimation approaches highly depends on the amount, coverage, and representation of valid samples in the training dataset. We present a dataset artificial augmentation approach with a small number of actual training samples by light source spectra tuning. Then, the reflectance estimation process is carried out with our augmented color samples for commonly used datasets (IES, Munsell, Macbeth, Leeds). Finally, the impact of the augmented color sample number is investigated using different augmented color sample numbers. The results show that our proposed approach can artificially augment the color samples from CCSG 140 color samples to 13791 color samples and even more. The reflectance estimation performances with augmented color samples are much higher than with the benchmark CCSG datasets for all tested datasets (IES, Munsell, Macbeth, Leeds, as well as a real-scene hyperspectral reflectance database). It indicates that the proposed dataset augmentation approach is practical for improving the reflectance estimation performances.

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A study on the use of thermal imaging as a diagnostic tool for the detection of digital dermatitis in dairy cattle

Cited by 15 publications

References 21 publications

Thermography for disease detection in livestock: A scoping review

Thermography for disease detection in livestock: A scoping review

Broad-spectrum infrared thermography for detection of M2 digital dermatitis lesions on hind feet of standing dairy cattle

Dataset artificial augmentation with a small number of training samples for reflectance estimation

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