首页|Research from Inner Mongolia University Has Provided New Study Findings on Suppo rt Vector Machines (Measurement of body size parameters and body weight predicti on in beef cattle based on image analysis)
Research from Inner Mongolia University Has Provided New Study Findings on Suppo rt Vector Machines (Measurement of body size parameters and body weight predicti on in beef cattle based on image analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on have been pub lished. According to news reporting from Hohhot, People’s Republic of China, by NewsRx journalists, research stated, “The body size parameter of cattle is an im portant index reflecting the growth and development and health condition of catt le. The traditional manual contact measurement is not only a large workload and difficult to measure, but also prone to problems such as affecting the normal li fe habits of cattle.” The news reporters obtained a quote from the research from Inner Mongolia Univer sity: “In this paper, we address this problem by proposing a contactless body si ze measurement method for cattle based on machine vision. Firstly, the cattle is confined to a fixed space using a position-limiting device, and images of the b ody of the cattle are taken from three directions: top, left, and right, using m ultiple cameras. Secondly, the image is segmented using a fuzzy clustering algor ithm based on neighborhood adaptive local spatial information improvement, and t he image is processed to extract the contour images of the top view and side vie w. The key points of body measurements were extracted using interval division an d curvature calculation for the side view images, and the key point information was extracted using skeleton extraction and pruning for the top view images, whi ch realized the measurements of body height(BH), rump height(RH), body slanting length(BSL), and abdominal circumference(AC) parameters of the cattle. The corre lation between body size and weight data obtained by contactless methods was inv estigated and the modeled using one-factor linear regression, one-factor nonline ar regression, multivariate stepwise regression, RBF network fitting, BP neural network fitting, support vector machine, and particle swarm optimization-based s upport vector machine methods, respectively. Information on body size parameters was collected from 137 cattles, and the results showed that the maximum errors between the measured and actual values of BH, RH, BSL and AC were 5.0% , 4.4%, 3.6%, and 5.5%, respectively. Cor relation of BH, RH, BSL and AC with weight obtained by non-contact methods was > 0.75.”
Inner Mongolia University, Hohhot, Peopl e’s Republic of China, Asia, Emerging Technologies, Machine Learning, Support Ve ctor Machines, Vector Machines
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