This paper proposes and discusses a methodology to evaluate the performance of automated mastitis-detection systems with respect to their practical value on farm. The protocols are based on 3 on-farm requirements: (1) to detect cows with clinical mastitis promptly and accurately to enable timely and appropriate treatment, (2) to identify cows with high somatic cell count to manage bulk milk SCC levels, and (3) to report the mastitis infection status of cows at the end of lactation to support decisions on individual cow dry-cow therapy. Separate protocols for each requirement are proposed and discussed, including gold standards, evaluation tests, performance indicators, and performance targets. Aspects that require further research or clarification are identified. Actual field data are used as examples. Further debate is invited, the aim being to achieve international agreement on how to evaluate and report performance of different mastitis-detection technologies. Better performance information will allow farmers to compare different mastitis-detection systems sensibly and fairly before investing. Also, the use of evaluation protocols should help technology providers to refine current, or develop new, automated mastitis-detection systems. Such developments are likely to accelerate adoption of these systems, potentially leading to improved animal health, milk quality, and labor productivity.
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http://dx.doi.org/10.3168/jds.2012-6190 | DOI Listing |
Animals (Basel)
June 2024
Powermilk, 81107 Kalloni Lesvos, Greece.
Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. Thermal imaging presents a promising avenue for non-invasive detection, but existing methodologies often rely on simplistic temperature differentials, potentially leading to inaccurate assessments. This study proposes an advanced algorithmic approach integrating thermal imaging processing with statistical texture analysis and t-distributed stochastic neighbor embedding (t-SNE).
View Article and Find Full Text PDFPrev Vet Med
November 2023
KU Leuven, Biosystems Department, Animal and Human Health Engineering Division, Campus Geel, Kleinhoefstraat 4, 2440 Geel, Belgium.
This study aims to describe the relation between farm-level management factors and estimated farm-level mastitis incidence and milk loss traits (MIMLT) at dairy farms with automated milking systems. In this observational study, 43 commercial dairy farms in Belgium and the Netherlands were included and 148 'management and udder health related variables' were obtained during a farm visit through a farm audit and survey. The MIMLT were estimated from milk yield data.
View Article and Find Full Text PDFAnimals (Basel)
July 2023
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
Thermal infrared technology is utilized for detecting mastitis in cows owing to its non-invasive and efficient characteristics. However, the presence of surrounding regions and obstacles can impede accurate temperature measurement, thereby compromising the effectiveness of dairy mastitis detection. To address these problems, we proposed the CLE-UNet (Centroid Loss Ellipticization UNet) semantic segmentation algorithm.
View Article and Find Full Text PDFAnimals (Basel)
June 2023
Department of Veterinary Sciences, University of Turin, 10095 Grugliasco, TO, Italy.
Automatic milking systems (AMS) have played a pioneering role in the advancement of Precision Livestock Farming, revolutionizing the dairy farming industry on a global scale. This review specifically targets papers that focus on the use of modeling approaches within the context of AMS. We conducted a thorough review of 60 articles that specifically address the topics of cows' health, production, and behavior/management Machine Learning (ML) emerged as the most widely used method, being present in 63% of the studies, followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic models (7%), and detection algorithms (7%).
View Article and Find Full Text PDFJ Dairy Sci
May 2023
Department of Animal Science, Cornell University, Ithaca, NY 14853. Electronic address:
In this study, we developed a machine learning framework to detect clinical mastitis (CM) at the current milking (i.e., the same milking) and predict CM at the next milking (i.
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