Prof Ronny Rachman Noor Discovers GPS and Facial Recognition Technology to Overcome Farm Livestock Thefts
Australia is one of the world’s major meat and dairy producing countries. It is no wonder that the number of livestock in Australia is very large. As an illustration, the total number of cattle in Australia is 24.4 million heads and the number of sheep reaches 68 million heads.
According to Professor of IPB University, Prof Ronny Rachman Noor, with such a large number of livestock, one of the serious problems faced by the Australian livestock industry is livestock theft, which causes huge losses.
“Victims of livestock theft are generally farmers who live in remote areas. From the official data released by the Australian government, every year it is estimated that there are 31 thousand livestock thefts that cause losses of AUD $ 50 million or equivalent to Rp 498,594,250,000, “said Prof Ronny.
Furthermore, he gave an example that the level of livestock theft in the state of New South Wales, one of Australia’s livestock centers, in the period 2015 to 2020 there had been 16,700 sheep and 1,800 cattle stolen.
“If we imagine livestock farming in Australia, we should not imagine livestock farming like in Indonesia where the number of livestock averages 1-3 heads per farmer and are generally kept in cages,” said the IPB University lecturer from the Faculty of Animal Sciences (Fapet).
While in Australia, he said, cattle and sheep are generally raised extensively in very large areas and are usually quite remote. This condition allows thieves to freely loot livestock.
Regarding livestock theft, Prof Ronny said, “Actually, the law in Australia is quite harsh for livestock thieves because if the thief is caught and processed in court, the perpetrator can be sentenced to up to 14 years,” he said.
For example, in one incident, the thieves managed to steal 700 sheep worth AUD $140,000 or Rp 1,396,063,900. According to him, with this scale of theft, it is certain that the thieves had careful planning and used livestock transport trucks.
“It is likely that they also have sophisticated information to determine the right time to steal so that it is not known by the owner.”
In addition, he continued, in order not to cause noise and sound from the stolen livestock, it is certain that the thieves understand very well how to handle sheep so that when transferred to the truck the stolen livestock does not have a problem.
Prof Roni said that the police who dealt with livestock theft in Australia had difficulties because farmers did not know exactly when their livestock were stolen. In addition, their livestock are not collected every day, but only at certain times of the year.
“To address the widespread theft in Australia, a livestock ear number connected to the Global Positioning System (GPS) has been developed. This advanced ear number can track the movement of stolen livestock while informing its location using satellites,” said Prof Ronny.
By using this advanced ear number, farmers can monitor the movement of their livestock. If there is a rapid movement of livestock, farmers can immediately report it to the police because there is a possibility of livestock theft in their farms. Furthermore, the police can use the GPS data to track the stolen livestock.
“Another technology used by Australia to overcome livestock theft is facial recognition technology specifically developed for livestock based on artificial intelligence technology that can be used to identify livestock individually,” said Prof Ronny.
According to Prof Ronny, the facial recognition technology for livestock focuses on variations in the shape of the snout. A cow’s nose has a different shape from one another, so it can be used as a differentiator as well as fingerprints in humans.
“The use of advanced technology such as facial recognition technology in cattle is still under development. But it seems that in the future this technology is very necessary because it will at least reduce the rate of cattle and sheep theft in Australia, which is at a very alarming level,” he concluded. (*/Rz) (IAAS/RUM)
