Home » Japanese team develops AI technology: analyzing calf blood information to predict future meat quality – AI Artificial Intelligence – cnBeta.COM

Japanese team develops AI technology: analyzing calf blood information to predict future meat quality – AI Artificial Intelligence – cnBeta.COM

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Japanese team develops AI technology: analyzing calf blood information to predict future meat quality – AI Artificial Intelligence – cnBeta.COM

Recently, Japanese media revealed that,A research team led by Kazuya Matsumoto, a professor of animal sciences at Kinki University in Japan, has developed a technology that uses artificial intelligence (AI) to analyze blood information of calves to predict the quality of beef when they grow up.

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Kyodo News reported on the 28th that the AI ​​​​analyzed 135 kinds of protein information in the blood of calves to predict the weight of half-scallop before finishing, as well as the size of tenderloin and the thickness of pork belly. In the future, if livestock farmers want to raise cattle with larger tenderloin, they can use AI prediction to select calves with more tender tenderloin and use corresponding feed. The error between the predicted result of pork belly thickness and the actual result was about 4%. The blood collection for analysis is said to be about 1 to 2 milliliters, which will not burden the calves.

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According to the report, the AI ​​prediction service fee is assumed to be about 20,000 yen (about 1,000 yuan) per cow, and it will be provided to livestock farmers through wholesalers within a few years at the earliest. “It is hoped that the AI ​​will not only produce predictions, but also advise[farmers]on how to raise cattle,” Matsumoto said.

Because the AI ​​can predict the meat quality of the calf when it grows up, wholesalers and livestock farmers can trade when the calf is still young without waiting for the auction in the wholesale market. According to the prediction of AI, veterinarians and feed companies can also propose relevant diagnosis and treatment methods and feed usage methods.

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