首页|New Machine Learning Research Has Been Reported by Researchers at China Agricult ural University (In Vivo Prediction of Breast Muscle Weight in Broiler Chickens Using X-ray Images Based on Deep Learning and Machine Learning)
New Machine Learning Research Has Been Reported by Researchers at China Agricult ural University (In Vivo Prediction of Breast Muscle Weight in Broiler Chickens Using X-ray Images Based on Deep Learning and Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, "Accurately es timating the breast muscle weight of broilers is important for poultry productio n. However, existing related methods are plagued by cumbersome processes and lim ited automation." Funders for this research include National Key Research And Development Program of China; National Key R&D Program of China. Our news journalists obtained a quote from the research from China Agricultural University: "To address these issues, this study proposed an efficient method fo r predicting the breast muscle weight of broilers. First, because existing deep learning models struggle to strike a balance between accuracy and memory consump tion, this study designed a multistage attention enhancement fusion segmentation network (MAEFNet) to automatically acquire pectoral muscle mask images from X-r ay images. MAEFNet employs the pruned MobileNetV3 as the encoder to efficiently capture features and adopts a novel decoder to enhance and fuse the effective fe atures at various stages. Next, the selected shape features were automatically e xtracted from the mask images. Finally, these features, including live weight, w ere input to the SVR (Support Vector Regression) model to predict breast muscle weight. MAEFNet achieved the highest intersection over union (96.35% ) with the lowest parameter count (1.51 M) compared to the other segmentation mo dels."
China Agricultural UniversityBeijingPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learni ngVivo
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