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Remote Sensing in Agriculture: State-of-the-Art
| Remote Sensing in Agriculture: State-of-the-Art |
| Autore | Borgogno-Mondino Enrico |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (220 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology Environmental science, engineering & technology |
| Soggetto non controllato |
feature selection
spectral angle mapper support vector machine support vector regression hyperspectral imaging UAV cross-scale yellow rust spatial resolution winter wheat MODIS northern Mongolia remote sensing indices spring wheat yield estimation UAV-based LiDAR biomass crop height field phenotyping oasis crop type mapping Sentinel-1 and 2 integration statistically homogeneous pixels (SHPs) red-edge spectral bands and indices recursive feature increment (RFI) random forest (RF) unmanned aerial vehicles (UAVs) remote sensing (RS) thermal UAV RS thermal infrared (TIR) precision agriculture (PA) crop water stress monitoring plant disease detection vegetation status monitoring Landsat data blending crop yield prediction gap-filling volumetric soil moisture synthetic aperture radar (SAR) Sentinel-1 soil moisture semi-empirical model soil moisture Karnataka India reflectance digital number (DN) vegetation index (VI) Parrot Sequoia (Sequoia) DJI Phantom 4 Multispectral (P4M) Synthetic Aperture Radar SAR lodging Hidden Markov Random Field HMRF CDL corn soybean crop Monitoring crop management apple orchard damage polarimetric decomposition entropy anisotropy alpha angle storm damage mapping economic loss insurance support |
| ISBN | 3-0365-5484-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Remote Sensing in Agriculture |
| Record Nr. | UNINA-9910637779903321 |
Borgogno-Mondino Enrico
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Smart Sensing Technologies for Agriculture
| Smart Sensing Technologies for Agriculture |
| Autore | Adamchuk Viacheslav |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (232 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
moisture measurement
Kalman filter model predictive control germination paper convolutional neural networks livestock lying posture standing posture Three-dimensional mapping quasi-3D inversion algorithm cation exchange capacity clay content sandy infertile soil optical micro-sensors crop protection precision agriculture infrared spectroscopy principal component analysis (PCA) partial least squares (PLS) droplet characterization apparent electrical conductivity (ECa) pH UAV boundary-line quantile regression law of minimum on-site detection ion-selective electrode (ISE) soil nitrate nitrogen (NO3−-N) soil moisture sensor fusion transfer learning deep learning body dimensions point cloud Kd-network feature recognition FFPH non-contact measurement X-ray fluorescence spectroscopy soil nutrients proximal soil sensing soil testing laser-induced breakdown spectroscopy LIBS elemental composition broiler surface temperature extraction thermal image processing head region locating adaptive K-means ellipse fitting harvesting robot gripper segmentation cutting point detection soil soil electrical resistivity autonomous robot real-time measurement precision farming mapping precision weeding multispectral imaging kinetic stereo imaging plant detection yield estimation machine vision willow tree |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557667303321 |
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Adamchuk Viacheslav
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||