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