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Image analysis, artificial intelligence will change dairy farming

Cow gait images allow early detection of serious diseases.

When dairy farmers are busy with routines such as cleaning cow sheds, milking and feeding, it becomes difficult to determine the condition of cows. If this continues, they may be too busy to ensure the quantity and quality of milk and dairy products.

A group of researchers led by professor Yagi Yasushi at the Institute of Scientific & Industrial Research at Osaka University in Japan, together with professor Nakada Ken at Rakuno Gakuen University, developed a technique for monitoring health of dairy cattle with high frequency and accuracy by using a camera and artificial intelligence with the aim of realizing "smart" cow housing (Figure).

Credit: Osaka University.

A group of researchers led by Osaka University developed an early detection method for cow lameness (hoof disease), a major disease of dairy cattle, from images of cow gait with an accuracy of 99% or higher by applying human gait analysis. This technique allows early detection of lameness from cow gait, which was previously difficult. It is hoped that a revolution in dairy farming can be achieved through detailed observation by artificial intelligence-powered image analysis.

Hoof health is an important aspect of proper dairy cattle care. Hoof injuries and illnesses, if left untreated, will lead to not only declining quantity and quality of dairy products but also life-threatening disease. Thus, early detection is very important.

Indicators for lameness are manifested in the back arch and gait patterns of cows. Methods for finding lameness by detecting back arch have been studied, and that method was effective in detecting moderate to severe lameness, Osaka University said.

The Yagi-led group established a method for the early detection of lameness based on cow gait images with 99% or greater accuracy by using their own human gait analysis technique. Specifically, this group waterproofed and dustproofed Microsoft Kinect, a camera-based sensor capable of measuring distance to an object, and set it in a cow shed at Rakuno Gakuen University. Based on the large number of cow gait images the sensor took, the group characterized cows' gaits and detected cows with lameness through machine learning.

"Our achievements will mark the start of techniques for monitoring cows using (artificial intelligence)-powered image analysis," Yagi said. "This will contribute largely to realizing a smart cow house interlocked with an automatic milking machine and feeding robot, both of which have already been introduced to some dairy farms, as well as wearable sensors attached to cows under study."

He added, "By finely adjusting the amount of expressed milk and the amount of feed, as well as by showing farmers cow conditions in detail through automatic analysis of cow conditions, we can realize a new era of dairy farming in which farmers can focus entirely on health management of their cows and delivering high-quality dairy products."

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