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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 electronic resource (283 p.) |
| Soggetto topico |
Research & information: general
Biology, life sciences Technology, engineering, agriculture |
| Soggetto non controllato |
artificial neural network (ANN)
Grain weevil identification neural modelling classification winter wheat grain artificial neural network ferulic acid deoxynivalenol nivalenol MLP network sensitivity analysis precision agriculture machine learning similarity metric memory deep learning plant growth dynamic response root zone temperature dynamic model NARX neural networks hydroponics vegetation indices UAV neural network corn plant density corn canopy cover yield prediction CLQ GA-BPNN GPP-driven spectral model rice phenology EBK correlation filter crop yield prediction hybrid feature extraction recursive feature elimination wrapper artificial neural networks big data classification high-throughput phenotyping modeling predicting time series forecasting soybean food production paddy rice mapping dynamic time warping LSTM weakly supervised learning cropland mapping apparent soil electrical conductivity (ECa) magnetic susceptibility (MS) EM38 neural networks Phoenix dactylifera L. Medjool dates image classification convolutional neural networks transfer learning average degree of coverage coverage unevenness coefficient optimization high-resolution imagery oil palm tree CNN Faster-RCNN image identification agroecology weeds yield gap environment health crop models soil and plant nutrition automated harvesting model application for sustainable agriculture remote sensing for agriculture decision supporting systems neural image analysis |
| 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 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 |
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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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