Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

• Autore: Qiao Yongliang
• Pubblicazione: Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
• Descrizione fisica: 1 electronic resource (228 p.)
• Soggetto topico: Technology: general issues; History of engineering & technology
• Soggetto non controllato: pig weight; body size; estimation; deep learning; convolutional neural network; pig identification; mask scoring R-CNN; soft-NMS; group-housed pigs; audio; dairy cow; mastication; jaw movement; forage management; precision livestock management; equine behavior; wearable sensor; intermodality interaction; class-balanced focal loss; absorbing Markov chain; cow behavior analysis; prediction of calving time; cow identification; EfficientDet; YOLACT++; cascaded model; instance segmentation; generative adversarial network; machine learning; automated medical image processing; deep neural network; animal science; CT scans; computer vision; cow; extensive livestock; sensorized wearable device; monitoring; parturition prediction; radar sensors; radar signal processing; animal farming; computational ethology; signal classification; wavelet analysis; dairy welfare; hierarchical clustering; mutual information; precision livestock farming; time budgets; unsupervised machine learning; wearables design; animal-centered design; animal telemetry; modularity; smart collar; design contributions; additive manufacturing; low-frequency tracking; commercial aviary; laying hens; false registrations; tree-based classifier; animal behaviour
• Persona (resp. second.): ChaiLilong; HeDongjian; SuDaobilige; QiaoYongliang
• Sommario/riassunto: Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management.
• Titolo autorizzato: Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910576879803321
• Lo trovi qui: Univ. Federico II