Automatic Temperature Measurement for Hot Spots in Face Region of Cattle using Infrared Thermography

Jaddoa, Mohammed Ahmed; Al-Jumaily, Adel; González, L. A.; Cuthbertson, Holly

doi:10.5220/0007810101960201

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…For example, if the refresh rate of a thermal imaging device is 50 Hz, this means that the image refreshes 50 times every second (equivalent to 50 frames/s. Only a minority of studies recorded using video ( 14 , 32 , 36 , 54 , 56 , 79 , 96 , 115 ) with just one quarter of those using a frame rate >50 Hz ( 32 , 96 ) due to thermal camera capabilities. The AAT also recommend that for temperature analysis, radiometric video files are used as there is temperature data embedded in each pixel.…”

Section: Resultsmentioning

confidence: 99%

“…Image analysis techniques have traditionally utilized manual techniques to highlight regions of interest ( 52 ) and commercial IR camera software for image processing ( 100 , 110 ). More recent studies have made advances in terms of advanced statistical predictive modeling ( 31 ) and algorithm development ( 79 ). To act autonomously and yield real-time results, it is likely that techniques incorporating computer vision and neural networks will be necessary.…”

Section: Discussionmentioning

confidence: 99%

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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%

Section: Discussionmentioning

confidence: 99%

Thermography for disease detection in livestock: A scoping review

et al. 2022

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“…The use of pulsed electric fields could improve energy and yield efficiency of tomato processing by using less energy in pressing tomatoes and reducing greenhouse gas emissions [44,45]. AI-and ML-based models and technologies are being used for sustainable livestock farming, such as monitoring animal health and well-being, predicting disease, and optimizing dietary supply, intake, and consumption of nutrients [46][47][48][49][50][51]. To determine where and how to target efforts most effectively, it is necessary to capture where along the food chain, in which foods, and in which countries the greatest losses occur [52].…”

Section: Sdg 2: Zero Hungermentioning

confidence: 99%

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

et al. 2022

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The United Nations’ Sustainable Development Goals (SDGs) set out to improve the quality of life of people in developed, emerging, and developing countries by covering social and economic aspects, with a focus on environmental sustainability. At the same time, data-driven technologies influence our lives in all areas and have caused fundamental economical and societal changes. This study presents a comprehensive literature review on how data-driven approaches have enabled or inhibited the successful achievement of the 17 SDGs to date. Our findings show that data-driven analytics and tools contribute to achieving the 17 SDGs, e.g., by making information more reliable, supporting better-informed decision-making, implementing data-based policies, prioritizing actions, and optimizing the allocation of resources. Based on a qualitative content analysis, results were aggregated into a conceptual framework, including the following categories: (1) uses of data-driven methods (e.g., monitoring, measurement, mapping or modeling, forecasting, risk assessment, and planning purposes), (2) resulting positive effects, (3) arising challenges, and (4) recommendations for action to overcome these challenges. Despite positive effects and versatile applications, problems such as data gaps, data biases, high energy consumption of computational resources, ethical concerns, privacy, ownership, and security issues stand in the way of achieving the 17 SDGs.

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“…The authors identified five features (clusters of data) using an ANN model, which showed strong separation between healthy and infected Ellis, Jacobs, Dijkstra, van Laar, Cant, Tulpan and Ferguson birds and were able to differentiate between healthy and infected birds with an accuracy of 66.6% on day 2 and 100% on day after infection. Similar to vocalization, infection may lead to detectable differences in movement patterns (Colles et al, 2016; optical flow analysis) and the surface temperature of animals (Jaddoa et al, 2019; IR thermal imaging), leading to earlier diagnosis of disease outbreak.…”

Section: Pattern Recognitionmentioning

confidence: 99%

Review: Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data

Ellis

Jacobs²,

Dijkstra

et al. 2020

Animal

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Mechanistic models (MMs) have served as causal pathway analysis and ‘decision-support’ tools within animal production systems for decades. Such models quantitatively define how a biological system works based on causal relationships and use that cumulative biological knowledge to generate predictions and recommendations (in practice) and generate/evaluate hypotheses (in research). Their limitations revolve around obtaining sufficiently accurate inputs, user training and accuracy/precision of predictions on-farm. The new wave in digitalization technologies may negate some of these challenges. New data-driven (DD) modelling methods such as machine learning (ML) and deep learning (DL) examine patterns in data to produce accurate predictions (forecasting, classification of animals, etc.). The deluge of sensor data and new self-learning modelling techniques may address some of the limitations of traditional MM approaches – access to input data (e.g. sensors) and on-farm calibration. However, most of these new methods lack transparency in the reasoning behind predictions, in contrast to MM that have historically been used to translate knowledge into wisdom. The objective of this paper is to propose means to hybridize these two seemingly divergent methodologies to advance the models we use in animal production systems and support movement towards truly knowledge-based precision agriculture. In order to identify potential niches for models in animal production of the future, a cross-species (dairy, swine and poultry) examination of the current state of the art in MM and new DD methodologies (ML, DL analytics) is undertaken. We hypothesize that there are several ways via which synergy may be achieved to advance both our predictive capabilities and system understanding, being: (1) building and utilizing data streams (e.g. intake, rumination behaviour, rumen sensors, activity sensors, environmental sensors, cameras and near IR) to apply MM in real-time and/or with new resolution and capabilities; (2) hybridization of MM and DD approaches where, for example, a ML framework is augmented by MM-generated parameters or predicted outcomes and (3) hybridization of the MM and DD approaches, where biological bounds are placed on parameters within a MM framework, and the DD system parameterizes the MM for individual animals, farms or other such clusters of data. As animal systems modellers, we should expand our toolbox to explore new DD approaches and big data to find opportunities to increase understanding of biological systems, find new patterns in data and move the field towards intelligent, knowledge-based precision agriculture systems.

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Automatic Temperature Measurement for Hot Spots in Face Region of Cattle using Infrared Thermography

Cited by 8 publications

References 12 publications

Thermography for disease detection in livestock: A scoping review

Thermography for disease detection in livestock: A scoping review

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

Review: Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data

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