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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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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 online resource (262 p.) |
| Soggetto topico |
Biology, life sciences
Research & information: general Technology, engineering, agriculture |
| Soggetto non controllato |
3D colonisation
aggressiveness Cereals colonization combinatory effects deoxynivalenol deoxynivalenol (DON) disease index disease index (DI) DNA methylation ear rot endophyte ergosterol F. culmorum F. graminearum flax Fo47 food safety forage FUM1 fumonisins fungi fusarium Fusarium Fusarium asiaticum fusarium damaged kernels (FDK) Fusarium graminearum Fusarium head blight Fusarium oxysporum Fusarium species Fusarium-damaged kernel horizontal cross-kingdom host-pathogen relations inoculation time and FHB response intestinal inflammation isolate effect keratomycosis LC-MS/MS maize Maize maize ear rot modelling monitoring mycotoxin mycotoxins n/a next-generation sequencing NF-κB nivalenol occurrence onychomycosis organic farming pathogenic and non-pathogenic strains pathogenicity phenotyping FHB photobiology PR genes proteomics resistance expression respiration sensitization silage silo soil minerals sowing value susceptibility window transcription factor trichothecene trichothecenes virulence wheat White collar complex wilt disease winter wheat zearalenone |
| 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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