Abstract
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.
Highlights
The global dairy industry growth has been steadily increasing in recent years, shown by the worldwide milk volume, reaching 513.23 million metric tons in 2020
The abrupt movements in this figure were reported as a variance of x and y axes; this figure shows that the 5- and 6-year-old cows had fewer movements in both directions (x and y axes) compared to younger and older cows
In the case of respiration rate (RR), all values recorded were below 60 BrPM, which is related to minimal stress [47], which is consistent with temperature–humidity index (THI) values for this study (Table 1)
Summary
The global dairy industry growth has been steadily increasing in recent years, shown by the worldwide milk volume, reaching 513.23 million metric tons in 2020. In Australia, dairy production represents one of the most important rural industries, with a market value of AUD 3.2 billion in 2018–2019 [4]. Several challenges related to climate change and logistics are expected to impact the dairy industry worldwide, such as (i) consumer concerns about animal welfare, (ii) warming environment and effect on animal welfare and productivity, (iii) increased resource costs and (iv) high-cost labor-related issues [5]. Different strategies have been devised to help increase the efficiency of dairy resource management and, the efficiency of production and quality traits of milk produced [5]
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