Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras

Fuentes, Sigfredo; Viejo, Claudia Gonzalez; Chauhan, Surinder S.; Joy, Aleena; Tongson, Eden; Dunshea, Frank R.

doi:10.3390/s20216334

Cited by 23 publications

(33 citation statements)

References 56 publications

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“…The HR in beats per minute (BPM) was estimated from the eye section of the cropped videos. The HR algorithm developed in Matlab ® R2018a and updated/adapted in version R2021a by the DAFW-UoM group is based on luminosity changes in the green color channel from ROIs obtained, which uses the photoplethysmography (PPG) principle based on the peak analysis of the signal obtained from luminosity over time, which computes the amplitude and frequency of this signal [ 38 , 44 ] ( Figure 3 a). Furthermore, the RR in breaths per minute (BrPM) was computed using the copped videos from the nose section.…”

Section: Methodsmentioning

confidence: 99%

Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence

Fuentes

Viejo

Tongson

et al. 2021

Sensors

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New and emerging technologies, especially those based on non-invasive video and thermal infrared cameras, can be readily tested on robotic milking facilities. In this research, implemented non-invasive computer vision methods to estimate cow’s heart rate, respiration rate, and abrupt movements captured using RGB cameras and machine learning modelling to predict eye temperature, milk production and quality are presented. RGB and infrared thermal videos (IRTV) were acquired from cows using a robotic milking facility. Results from 102 different cows with replicates (n = 150) showed that an artificial neural network (ANN) model using only inputs from RGB cameras presented high accuracy (R = 0.96) in predicting eye temperature (°C), using IRTV as ground truth, daily milk productivity (kg-milk-day−1), cow milk productivity (kg-milk-cow−1), milk fat (%) and milk protein (%) with no signs of overfitting. The ANN model developed was deployed using an independent 132 cow samples obtained on different days, which also rendered high accuracy and was similar to the model development (R = 0.93). This model can be easily applied using affordable RGB camera systems to obtain all the proposed targets, including eye temperature, which can also be used to model animal welfare and biotic/abiotic stress. Furthermore, these models can be readily deployed in conventional dairy farms.

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Section: Methodsmentioning

confidence: 99%

Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence

Fuentes

Viejo

Tongson

et al. 2021

Sensors

Self Cite

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“…Furthermore, classified data were reanalyzed by three different functions and a second model using Bayesian Regularized algorithm employed these inputs to predict HR. These models performed an accuracy of 85% and 84% respectively [44].…”

Section: Sheepmentioning

confidence: 95%

Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review

Debauche¹,

Elmoulat²,

Mahmoudi³

et al. 2021

RIA

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Numerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to advance our understanding of animal’s behaviors.

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“…The use of machine learning (ML) has been applied to different industries such as sustainability of materials [ 36 ], techno-economics [ 37 ], molecular crystals engineering [ 38 ], energy [ 39 ], diagnostics in medicine [ 40 ] and, more recently, food/beverages [ 17 , 18 , 22 , 29 , 41 ] and agriculture [ 42 , 43 , 44 ]. This has been an effective tool to aid in the prediction and rapid assessment of products; however, a common issue found when using ML is the overfitting of the models because the generalization of the data is not achieved.…”

Section: Introductionmentioning

confidence: 99%

Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity

Viejo

Tongson

Fuentes

2021

Sensors

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Aroma is one of the main attributes that consumers consider when appreciating and selecting a coffee; hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evaluation, which is time-consuming and requires highly trained assessors to avoid subjectivity. Therefore, this study aimed to estimate the coffee intensity and aromas using a low-cost and portable electronic nose (e-nose) and machine learning modeling. For this purpose, triplicates of six commercial coffee samples with different intensity levels were used for this study. Two machine learning models were developed based on artificial neural networks using the data from the e-nose as inputs to (i) classify the samples into low, medium, and high-intensity (Model 1) and (ii) to predict the relative abundance of 45 different aromas (Model 2). Results showed that it is possible to estimate the intensity of coffees with high accuracy (98%; Model 1), as well as to predict the specific aromas obtaining a high correlation coefficient (R = 0.99), and no under- or over-fitting of the models were detected. The proposed contactless, nondestructive, rapid, reliable, and low-cost method showed to be effective in evaluating volatile compounds in coffee, which is a potential technique to be applied within all stages of the production process to detect any undesirable characteristics on–time and ensure high-quality products.

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Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras

Cited by 23 publications

References 56 publications

Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence

Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence

Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review

Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity

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