Artificial Neural Networks in Agriculture
| Weitere Verfasser: |
Kujawa, Sebastian
, [HerausgeberIn]
Niedbała, Gniewko , [HerausgeberIn] |
|---|---|
| Umfang/Format: |
1 online resource (283 pages). |
| Schlagworte: | |
| Online Zugang: |
DOAB: download the publication DOAB: description of the publication |
| LEADER | 04613namaa2201477ui 4500 | ||
|---|---|---|---|
| 001 | 003028041 | ||
| 005 | 20221228154415.0 | ||
| 003 | DE-2553 | ||
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| 008 | 20220111s2021 xx |||||o ||| 0|eng d | ||
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| 024 | 7 | |a 10.3390/books978-3-0365-1579-3 |c doi | |
| 041 | 0 | |a eng | |
| 042 | |a dc | ||
| 072 | 7 | |a GP |2 bicssc | |
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| 072 | 7 | |a T |2 bicssc | |
| 100 | 1 | |a Kujawa, Sebastian |e editor | |
| 264 | |b MDPI - Multidisciplinary Digital Publishing Institute, |c 2021. | ||
| 700 | 1 | |a Niedbała, Gniewko |e editor | |
| 700 | 1 | |a Kujawa, Sebastian |e other | |
| 700 | 1 | |a Niedbała, Gniewko |e other | |
| 245 | 1 | 0 | |a Artificial Neural Networks in Agriculture |
| 300 | |a 1 online resource (283 pages). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
| 546 | |a English | ||
| 650 | 7 | |a Research & information: general |2 bicssc | |
| 650 | 7 | |a Biology, life sciences |2 bicssc | |
| 650 | 7 | |a Technology, engineering, agriculture |2 bicssc | |
| 653 | |a artificial neural network (ANN) | ||
| 653 | |a Grain weevil identification | ||
| 653 | |a neural modelling classification | ||
| 653 | |a winter wheat | ||
| 653 | |a grain | ||
| 653 | |a artificial neural network | ||
| 653 | |a ferulic acid | ||
| 653 | |a deoxynivalenol | ||
| 653 | |a nivalenol | ||
| 653 | |a MLP network | ||
| 653 | |a sensitivity analysis | ||
| 653 | |a precision agriculture | ||
| 653 | |a machine learning | ||
| 653 | |a similarity | ||
| 653 | |a metric | ||
| 653 | |a memory | ||
| 653 | |a deep learning | ||
| 653 | |a plant growth | ||
| 653 | |a dynamic response | ||
| 653 | |a root zone temperature | ||
| 653 | |a dynamic model | ||
| 653 | |a NARX neural networks | ||
| 653 | |a hydroponics | ||
| 653 | |a vegetation indices | ||
| 653 | |a UAV | ||
| 653 | |a neural network | ||
| 653 | |a corn plant density | ||
| 653 | |a corn canopy cover | ||
| 653 | |a yield prediction | ||
| 653 | |a CLQ | ||
| 653 | |a GA-BPNN | ||
| 653 | |a GPP-driven spectral model | ||
| 653 | |a rice phenology | ||
| 653 | |a EBK | ||
| 653 | |a correlation filter | ||
| 653 | |a crop yield prediction | ||
| 653 | |a hybrid feature extraction | ||
| 653 | |a recursive feature elimination wrapper | ||
| 653 | |a artificial neural networks | ||
| 653 | |a big data | ||
| 653 | |a classification | ||
| 653 | |a high-throughput phenotyping | ||
| 653 | |a modeling | ||
| 653 | |a predicting | ||
| 653 | |a time series forecasting | ||
| 653 | |a soybean | ||
| 653 | |a food production | ||
| 653 | |a paddy rice mapping | ||
| 653 | |a dynamic time warping | ||
| 653 | |a LSTM | ||
| 653 | |a weakly supervised learning | ||
| 653 | |a cropland mapping | ||
| 653 | |a apparent soil electrical conductivity (ECa) | ||
| 653 | |a magnetic susceptibility (MS) | ||
| 653 | |a EM38 | ||
| 653 | |a neural networks | ||
| 653 | |a Phoenix dactylifera L. | ||
| 653 | |a Medjool dates | ||
| 653 | |a image classification | ||
| 653 | |a convolutional neural networks | ||
| 653 | |a transfer learning | ||
| 653 | |a average degree of coverage | ||
| 653 | |a coverage unevenness coefficient | ||
| 653 | |a optimization | ||
| 653 | |a high-resolution imagery | ||
| 653 | |a oil palm tree | ||
| 653 | |a CNN | ||
| 653 | |a Faster-RCNN | ||
| 653 | |a image identification | ||
| 653 | |a agroecology | ||
| 653 | |a weeds | ||
| 653 | |a yield gap | ||
| 653 | |a environment | ||
| 653 | |a health | ||
| 653 | |a crop models | ||
| 653 | |a soil and plant nutrition | ||
| 653 | |a automated harvesting | ||
| 653 | |a model application for sustainable agriculture | ||
| 653 | |a remote sensing for agriculture | ||
| 653 | |a decision supporting systems | ||
| 653 | |a neural image analysis | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/4046 |7 0 |z DOAB: download the publication |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/76601 |7 0 |z DOAB: description of the publication |
| 590 | |a Online publication | ||
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