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

Live sheep export has become a public concern. This study aimed to test a non-contact biometric system based on artificial intelligence to assess heat stress of sheep to be potentially used as automated animal welfare assessment in farms and while in transport. Skin temperature (°C) from head features were extracted from infrared thermal videos (IRTV) using automated tracking algorithms. Two parameter engineering procedures from RGB videos were performed to assess Heart Rate (HR) in beats per minute (BPM) and respiration rate (RR) in breaths per minute (BrPM): (i) using changes in luminosity of the green (G) channel and (ii) changes in the green to red (a) from the CIELAB color scale. A supervised machine learning (ML) classification model was developed using raw RR parameters as inputs to classify cutoff frequencies for low, medium, and high respiration rate (Model 1). A supervised ML regression model was developed using raw HR and RR parameters from Model 1 (Model 2). Results showed that Models 1 and 2 were highly accurate in the estimation of RR frequency level with 96% overall accuracy (Model 1), and HR and RR with R = 0.94 and slope = 0.76 (Model 2) without statistical signs of overfitting

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

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

Livestock Identification Using Deep Learning for Traceability

Farm livestock identification and welfare assessment using non-invasive digital technology have gained interest in agriculture in the last decade, especially for accurate traceability. This study aimed to develop a face recognition system for dairy farm cows using advanced deep-learning models and computer vision techniques. This approach is non-invasive and potentially applicable to other farm animals of importance for identification and welfare assessment. The video analysis pipeline follows standard human face recognition systems made of four significant steps: (i) face detection, (ii) face cropping, (iii) face encoding, and (iv) face lookup. Three deep learning (DL) models were used within the analysis pipeline: (i) face detector, (ii) landmark predictor, and (iii) face encoder. All DL models were finetuned through transfer learning on a dairy cow dataset collected from a robotic dairy farm located in the Dookie campus at The University of Melbourne, Australia. Results showed that the accuracy across videos from 89 different dairy cows achieved an overall accuracy of 84%. The computer program developed may be deployed on edge devices, and it was tested on NVIDIA Jetson Nano board with a camera stream. Furthermore, it could be integrated into welfare assessment previously developed by our research group

Animal biometric assessment using non-invasive computer vision and machine learning are good predictors of dairy cows age and welfare: The future of automated veterinary support systems

Digitally extracted biometrics from visible videos of farm animals could be used to automatically assess animal welfare, contributing to the future of automated veterinary support systems. This study proposed using non-invasive video acquisition and biometric analysis of dairy cows in a robotic dairy farm (RDF) located at the Dookie campus, The University of Melbourne, Australia. Data extracted from dairy cows were used to develop two machine learning models: a biometrics regression model (Model 1) targeting (i) somatic cell count, (ii) weight, (iii) rumination, and (iv) feed intake and a classification model (Model 2) mapping features from dairy cow's face to predict animal age. Results showed that Model 1 achieved a high correlation coefficient (R = 0.96), slope (b = 0.96), and performance, and Model 2 had high accuracy (98%), low error (2%), and high performance without signs of under or overfitting. Models developed in this study can be used in parallel with other models to assess milk productivity, quality traits, and welfare for RDF and conventional dairy farms

Assessment of smoke contamination in grapevine berries and taint in wines due to bushfires using a low-cost E-nose and an artificial intelligence approach

Bushfires are increasing in number and intensity due to climate change. A newly developed low-cost electronic nose (e-nose) was tested on wines made from grapevines exposed to smoke in field trials. E-nose readings were obtained from wines from five experimental treatments: (i) low-density smoke exposure (LS), (ii) high-density smoke exposure (HS), (iii) high-density smoke exposure with in-canopy misting (HSM), and two controls: (iv) control (C; no smoke treatment) and (v) control with in-canopy misting (CM; no smoke treatment). These e-nose readings were used as inputs for machine learning algorithms to obtain a classification model, with treatments as targets and seven neurons, with 97% accuracy in the classification of 300 samples into treatments as targets (Model 1). Models 2 to 4 used 10 neurons, with 20 glycoconjugates and 10 volatile phenols as targets, measured: in berries one hour after smoke (Model 2; R = 0.98; R2 = 0.95; b = 0.97); in berries at harvest (Model 3; R = 0.99; R2 = 0.97; b = 0.96); in wines (Model 4; R = 0.99; R2 = 0.98; b = 0.98). Model 5 was based on the intensity of 12 wine descriptors determined via a consumer sensory test (Model 5; R = 0.98; R2 = 0.96; b = 0.97). These models could be used by winemakers to assess near real-time smoke contamination levels and to implement amelioration strategies to minimize smoke taint in wines following bushfires.Sigfredo Fuentes, Vasiliki Summerson, Claudia Gonzalez Viejo, Eden Tongson, Nir Lipovetzky, Kerry L. Wilkinson ... et al

Protected areas and poverty

Protected areas are controversial because they are so important for conservation and because they distribute fortune and misfortune unevenly. The nature of that distribution, as well as the terrain of protected areas themselves, have been vigorously contested. In particular, the relationship between protected areas and poverty is a long-running debate in academic and policy circles. We review the origins of this debate and chart its key moments. We then outline the continuing flashpoints and ways in which further evaluation studies could improve the evidence base for policy-making and conservation practice

Urban Green Infrastructure Monitoring Using Remote Sensing from Integrated Visible and Thermal Infrared Cameras Mounted on a Moving Vehicle

Climate change forecasts higher temperatures in urban environments worsening the urban heat island effect (UHI). Green infrastructure (GI) in cities could reduce the UHI by regulating and reducing ambient temperatures. Forest cities (i.e., Melbourne, Australia) aimed for large-scale planting of trees to adapt to climate change in the next decade. Therefore, monitoring cities' green infrastructure requires close assessment of growth and water status at the tree-by-tree resolution for its proper maintenance and needs to be automated and efficient. This project proposed a novel monitoring system using an integrated visible and infrared thermal camera mounted on top of moving vehicles. Automated computer vision algorithms were used to analyze data gathered at an Elm trees avenue in the city of Melbourne, Australia (n = 172 trees) to obtain tree growth in the form of effective leaf area index (LAIe) and tree water stress index (TWSI), among other parameters. Results showed the tree-by-tree variation of trees monitored (5.04 km) between 2016-2017. The growth and water stress parameters obtained were mapped using customized codes and corresponded with weather trends and urban management. The proposed urban tree monitoring system could be a useful tool for city planning and GI monitoring, which can graphically show the diurnal, spatial, and temporal patterns of change of LAIe and TWSI to monitor the effects of climate change on the GI of cities

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

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 nine 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

Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence

: Wine aroma profiles are determinant for the specific style and quality characteristics of final wines. These are dependent on the seasonality, mainly weather conditions, such as solar exposure and temperatures and water management strategies from veraison to harvest. This paper presents machine learning modeling strategies using weather and water management information from a Pinot noir vineyard from 2008 to 2016 vintages as inputs and aroma profiles from wines from the same vintages assessed using gas chromatography and chemometric analyses of wines as targets. The results showed that artificial neural network (ANN) models rendered the high accuracy in the prediction of aroma profiles (Model 1; R = 0.99) and chemometric wine parameters (Model 2; R = 0.94) with no indication of overfitting. These models could offer powerful tools to winemakers to assess the aroma profiles of wines before winemaking, which could help adjust some techniques to maintain/increase the quality of wines or wine styles that are characteristic of specific vineyards or regions. These models can be modified for different cultivars and regions by including more data from vertical vintages to implement artificial intelligence in winemaking

A Digital Approach to Evaluate the Effect of Berry Cell Death on Pinot Noir Wines' Quality Traits and Sensory Profiles Using Non-Destructive Near-Infrared Spectroscopy

Berry cell death (BCD) is linked to the development of flavors and aromas in berries and wine. The BCD pattern and rate within a growing season start at around 90–100 days after anthesis (DAA), and the rate until harvest depends on environmental factors. This study assessed the BCD effects on berry and wine composition from a boutique commercial vineyard in Victoria, Australia, using fluorescent imaging. Results showed differences in wine sensory profiles from the two blocks studied, mainly related to variations in BCD, due to differences in altitude between blocks. Furthermore, two machine learning (ML) models were constructed using near-infrared spectroscopy (NIR) measurements from full berries as inputs and living tissue (LT) and dead tissue (DT) from berries as targets (Model 1). Model 2 was developed using Brix, LT, DT from the east and west sides of canopies as inputs and using 19 sensory descriptors from wines as targets. High correlation and performances were achieved for both models without signs of overfitting (R = 0.94 and R = 0.80, respectively). These models could be used for decision-making purposes as an objective and comprehensive berry maturity assessment obtained in a non-destructive, accurate, and in a real-time fashion close to harvest, to secure specific wine styles