| 000 | 05212naaaa2201333uu 4500 | ||
|---|---|---|---|
| 001 | https://directory.doabooks.org/handle/20.500.12854/76601 | ||
| 005 | 20220220025059.0 | ||
| 020 | _abooks978-3-0365-1579-3 | ||
| 020 | _a9783036515809 | ||
| 020 | _a9783036515793 | ||
| 024 | 7 |
_a10.3390/books978-3-0365-1579-3 _cdoi |
|
| 041 | 0 | _aEnglish | |
| 042 | _adc | ||
| 072 | 7 |
_aGP _2bicssc |
|
| 072 | 7 |
_aPS _2bicssc |
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| 072 | 7 |
_aT _2bicssc |
|
| 100 | 1 |
_aKujawa, Sebastian _4edt |
|
| 700 | 1 |
_aNiedbała, Gniewko _4edt |
|
| 700 | 1 |
_aKujawa, Sebastian _4oth |
|
| 700 | 1 |
_aNiedbała, Gniewko _4oth |
|
| 245 | 1 | 0 | _aArtificial Neural Networks in Agriculture |
| 260 |
_aBasel, Switzerland _bMDPI - Multidisciplinary Digital Publishing Institute _c2021 |
||
| 300 | _a1 electronic resource (283 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aModern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by/4.0/ _2cc _4https://creativecommons.org/licenses/by/4.0/ |
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| 546 | _aEnglish | ||
| 650 | 7 |
_aResearch & information: general _2bicssc |
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| 650 | 7 |
_aBiology, life sciences _2bicssc |
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| 650 | 7 |
_aTechnology, engineering, agriculture _2bicssc |
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| 653 | _aartificial neural network (ANN) | ||
| 653 | _aGrain weevil identification | ||
| 653 | _aneural modelling classification | ||
| 653 | _awinter wheat | ||
| 653 | _agrain | ||
| 653 | _aartificial neural network | ||
| 653 | _aferulic acid | ||
| 653 | _adeoxynivalenol | ||
| 653 | _anivalenol | ||
| 653 | _aMLP network | ||
| 653 | _asensitivity analysis | ||
| 653 | _aprecision agriculture | ||
| 653 | _amachine learning | ||
| 653 | _asimilarity | ||
| 653 | _ametric | ||
| 653 | _amemory | ||
| 653 | _adeep learning | ||
| 653 | _aplant growth | ||
| 653 | _adynamic response | ||
| 653 | _aroot zone temperature | ||
| 653 | _adynamic model | ||
| 653 | _aNARX neural networks | ||
| 653 | _ahydroponics | ||
| 653 | _avegetation indices | ||
| 653 | _aUAV | ||
| 653 | _aneural network | ||
| 653 | _acorn plant density | ||
| 653 | _acorn canopy cover | ||
| 653 | _ayield prediction | ||
| 653 | _aCLQ | ||
| 653 | _aGA-BPNN | ||
| 653 | _aGPP-driven spectral model | ||
| 653 | _arice phenology | ||
| 653 | _aEBK | ||
| 653 | _acorrelation filter | ||
| 653 | _acrop yield prediction | ||
| 653 | _ahybrid feature extraction | ||
| 653 | _arecursive feature elimination wrapper | ||
| 653 | _aartificial neural networks | ||
| 653 | _abig data | ||
| 653 | _aclassification | ||
| 653 | _ahigh-throughput phenotyping | ||
| 653 | _amodeling | ||
| 653 | _apredicting | ||
| 653 | _atime series forecasting | ||
| 653 | _asoybean | ||
| 653 | _afood production | ||
| 653 | _apaddy rice mapping | ||
| 653 | _adynamic time warping | ||
| 653 | _aLSTM | ||
| 653 | _aweakly supervised learning | ||
| 653 | _acropland mapping | ||
| 653 | _aapparent soil electrical conductivity (ECa) | ||
| 653 | _amagnetic susceptibility (MS) | ||
| 653 | _aEM38 | ||
| 653 | _aneural networks | ||
| 653 | _aPhoenix dactylifera L. | ||
| 653 | _aMedjool dates | ||
| 653 | _aimage classification | ||
| 653 | _aconvolutional neural networks | ||
| 653 | _atransfer learning | ||
| 653 | _aaverage degree of coverage | ||
| 653 | _acoverage unevenness coefficient | ||
| 653 | _aoptimization | ||
| 653 | _ahigh-resolution imagery | ||
| 653 | _aoil palm tree | ||
| 653 | _aCNN | ||
| 653 | _aFaster-RCNN | ||
| 653 | _aimage identification | ||
| 653 | _aagroecology | ||
| 653 | _aweeds | ||
| 653 | _ayield gap | ||
| 653 | _aenvironment | ||
| 653 | _ahealth | ||
| 653 | _acrop models | ||
| 653 | _asoil and plant nutrition | ||
| 653 | _aautomated harvesting | ||
| 653 | _amodel application for sustainable agriculture | ||
| 653 | _aremote sensing for agriculture | ||
| 653 | _adecision supporting systems | ||
| 653 | _aneural image analysis | ||
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/4046 _70 _zDOAB: download the publication |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/76601 _70 _zDOAB: description of the publication |
| 999 |
_c60536 _d60536 |
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