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Artificial Neural Networks in Agriculture
| Artificial Neural Networks in Agriculture |
| Autore | Kujawa Sebastian |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (283 p.) |
| Soggetto topico |
Biology, life sciences
Research & information: general Technology, engineering, agriculture |
| Soggetto non controllato |
agroecology
apparent soil electrical conductivity (ECa) artificial neural network artificial neural network (ANN) artificial neural networks automated harvesting average degree of coverage big data classification CLQ CNN convolutional neural networks corn canopy cover corn plant density correlation filter coverage unevenness coefficient crop models crop yield prediction cropland mapping decision supporting systems deep learning deoxynivalenol dynamic model dynamic response dynamic time warping EBK EM38 environment Faster-RCNN ferulic acid food production GA-BPNN GPP-driven spectral model grain Grain weevil identification health high-resolution imagery high-throughput phenotyping hybrid feature extraction hydroponics image classification image identification LSTM machine learning magnetic susceptibility (MS) Medjool dates memory metric MLP network model application for sustainable agriculture modeling NARX neural networks neural image analysis neural modelling classification neural network neural networks nivalenol oil palm tree optimization paddy rice mapping Phoenix dactylifera L. plant growth precision agriculture predicting recursive feature elimination wrapper remote sensing for agriculture rice phenology root zone temperature sensitivity analysis similarity soil and plant nutrition soybean time series forecasting transfer learning UAV vegetation indices weakly supervised learning weeds winter wheat yield gap yield prediction |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557509803321 |
Kujawa Sebastian
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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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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