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 online resource (228 p.)
• Soggetto topico: History of engineering & technology; Technology: general issues
• Soggetto non controllato: absorbing Markov chain; additive manufacturing; animal behaviour; animal farming; animal science; animal telemetry; animal-centered design; audio; automated medical image processing; body size; cascaded model; class-balanced focal loss; commercial aviary; computational ethology; computer vision; convolutional neural network; cow; cow behavior analysis; cow identification; CT scans; dairy cow; dairy welfare; deep learning; deep neural network; design contributions; EfficientDet; equine behavior; estimation; extensive livestock; false registrations; forage management; generative adversarial network; group-housed pigs; hierarchical clustering; instance segmentation; intermodality interaction; jaw movement; laying hens; low-frequency tracking; machine learning; mask scoring R-CNN; mastication; modularity; monitoring; mutual information; parturition prediction; pig identification; pig weight; precision livestock farming; precision livestock management; prediction of calving time; radar sensors; radar signal processing; sensorized wearable device; signal classification; smart collar; soft-NMS; time budgets; tree-based classifier; unsupervised machine learning; wavelet analysis; wearable sensor; wearables design; YOLACT++
• 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