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Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming
| Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming |
| Autore | Qiao Yongliang |
| Pubbl/distr/stampa | 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 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910576879803321 |
Qiao Yongliang
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Electronics, Close-Range Sensors and Artificial Intelligence in Forestry
| Electronics, Close-Range Sensors and Artificial Intelligence in Forestry |
| Autore | Borz Stelian Alexandru |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (248 p.) |
| Soggetto topico |
Research & information: general
Biology, life sciences Forestry & related industries |
| Soggetto non controllato |
forest fire detection
deep learning ensemble learning Yolov5 EfficientDet EfficientNet big data automation artificial intelligence multi-modality acceleration classification events performance motor-manual felling willow Romania region detection of forest fire grading of forest fire weakly supervised loss fine segmentation region-refining segmentation lightweight Faster R-CNN ultrasound sensors road scanner terrestrial laser scanning TLS forest road maintenance forest road monitoring crowned road surface digital twinning climate smart LiDAR digitalization forest loss land-cover change machine learning spatial heterogeneity random forest model geographically weighted regression aboveground biomass estimation remote sensing Sentinel-2 Iran multiple regression artificial neural network k-nearest neighbor random forest canopy drone leaf leaves foliar samples sampling Aerial robotics UAS UAV IoT forest ecology accessibility wood diameter length close-range sensing Augmented Reality comparison accuracy effectiveness potential forestry 4.0 wood technology sawmilling productivity prediction long-term tree ring forestry detection resistance sensor micro-drilling resistance method signal processing Signal-to-Noise Ratio (SNR) |
| ISBN | 3-0365-6171-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910639985003321 |
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Borz Stelian Alexandru
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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