Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
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 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++ |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910576879803321 |
Qiao Yongliang
|
||
| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 |
|
Borz Stelian Alexandru
|
||
| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||