Teat traumatization in conventional and automatic milking system

Kunc, P.; Knı́žková, I.; Hanusová, Jana

doi:10.21611/qirt.2018.p4

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…For instance, RGB cameras can identify changes in color and movement, however precision is lost when monitoring multiple animals at once, as each individual occupies fewer pixels in the video frame (Wu et al, 2023). Infrared thermography cameras have the ability to measure and image the heat radiation in the infrared part of the light spectrum emitted by any object, so it can offer insights regarding temperature deviations on an animal's body (Kunc et al, 2007). Finally, 3D cameras are able to reconstruct the animal's anatomy (totally or partially), which is useful to analyze anatomical asymmetries (Abdul Jabbar et al, 2017).…”

Section: Computer Vision Techniquesmentioning

confidence: 99%

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Curti,

Selli,

Pinto

et al. 2023

Anim. Reprod.

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Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations.

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Section: Computer Vision Techniquesmentioning

confidence: 99%

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Curti,

Selli,

Pinto

et al. 2023

Anim. Reprod.

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“…Using thermal imaging has led to significant results in detecting subclinical mastitis in dairy sheep and cows. Kunc et al [5] proposed IRT as a non-invasive tool for evaluating the milking process, including its potential for early mastitis detection in dairy cows. Colak et al [6] demonstrated a strong correlation between the udder skin surface temperature (USST) and the results of the California mastitis test (CMT) for 94 cows.…”

Section: Introductionmentioning

confidence: 99%

Thermal Imaging and Dimensionality Reduction Techniques for Subclinical Mastitis Detection in Dairy Sheep

Tselios,

Alexandropoulos,

Pantopoulos

et al. 2024

Animals

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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). Our method achieves a high classification accuracy of 84% using the support vector machines (SVM) algorithm. Furthermore, we introduce another commonly employed evaluation metric, correlating thermal images with commercial California mastitis test (CMT) results after establishing threshold conditions on statistical features, yielding a sensitivity (the true positive rate) of 80% and a specificity (the true negative rate) of 92.5%. The evaluation metrics underscore the efficacy of our approach in detecting subclinical mastitis in dairy sheep, offering a robust tool for improved management practices.

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Teat traumatization in conventional and automatic milking system

Cited by 2 publications

References 2 publications

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Thermal Imaging and Dimensionality Reduction Techniques for Subclinical Mastitis Detection in Dairy Sheep

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