Shape identification and particles size distribution from basic shape parameters using ImageJ
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Controlling horse heart rate as a basis for training improvement
Computers and Electronics in Agriculture
A model for monitoring the condition of young pigs by their drinking behaviour
Computers and Electronics in Agriculture
Modelling the drinking patterns of young pigs using a state space model
Computers and Electronics in Agriculture
Automatic identification of marked pigs in a pen using image pattern recognition
Computers and Electronics in Agriculture
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Every year over 59billion animals are slaughtered for worldwide food production. The increasing demand for animal products has made mass animal breeding more important than ever. Satisfying the needs of the market, farmers will have to use automatic tools to monitor the welfare and health of their animals since manual monitoring is expensive and time consuming. Literature has shown that water use of pigs relates to important variables such as inside temperature, food intake, food conversion, growth rate and health condition. So, water use might be an interesting indicator for automatic monitoring pigs' health or productivity status. Therefore, we tried to find a cheap and elegant way to monitor continuous water use in a group of pigs in a farm pen. This study comprised four groups of piglets, each group of ten animals in a pen. On average, in the beginning of experiments pigs had a weight of 27kg and in the end they gained weight up to 40kg. Using a water-meter for each pen, water use rate was measured and monitored minutely. The pig house was also equipped with Charge-coupled device (CCD) cameras. Each pen was monitored for 13days using a camera which was installed above the pen to generate top-view images. There was a water outlet in the corner of each pen. Employing image processing algorithms, drink nipple visits were monitored automatically. Using data of a performed experiment comprising three weeks of data recordings, the relationship between water use and drink nipple visits was investigated. Results showed that by developing a data-based dynamic model of the visits to the drink nipple observed in videos, half-hourly water use could be estimated with an accuracy of 92%.