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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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Fusarium : Mycotoxins, Taxonomy and Pathogenicity
| Fusarium : Mycotoxins, Taxonomy and Pathogenicity |
| Autore | Stępień Łukasz |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (262 p.) |
| Soggetto topico |
Research & information: general
Biology, life sciences Technology, engineering, agriculture |
| Soggetto non controllato |
Fusarium head blight
Fusarium species soil minerals mycotoxins organic farming sowing value winter wheat Maize Fusarium monitoring forage silage maize ear rot nivalenol fumonisins flax Fusarium oxysporum pathogenic and non-pathogenic strains sensitization DNA methylation PR genes ear rot maize FUM1 pathogenicity virulence Fusarium graminearum next-generation sequencing proteomics photobiology transcription factor White collar complex Fusarium asiaticum colonization endophyte Fo47 wilt disease fusarium LC-MS/MS mycotoxin occurrence wheat trichothecene NF-κB intestinal inflammation combinatory effects food safety resistance expression aggressiveness F. graminearum F. culmorum isolate effect disease index Fusarium-damaged kernel deoxynivalenol susceptibility window inoculation time and FHB response keratomycosis onychomycosis horizontal cross-kingdom disease index (DI) fusarium damaged kernels (FDK) deoxynivalenol (DON) host-pathogen relations phenotyping FHB Cereals silo fungi modelling 3D colonisation respiration ergosterol zearalenone trichothecenes |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Fusarium |
| Record Nr. | UNINA-9910557660703321 |
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Stępień Łukasz
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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