Beschreibung: Artificial Neural Networks in Agriculture

Artificial Neural Networks in Agriculture

Weitere Verfasser:	Kujawa, Sebastian , [HerausgeberIn] Niedbała, Gniewko , [HerausgeberIn]
Umfang/Format:	1 online resource (283 pages).
Schlagworte:	Research & information: general Biology, life sciences Technology, engineering, agriculture 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
Online Zugang:	DOAB: download the publication DOAB: description of the publication

ISBN:	books978-3-0365-1579-3 9783036515809 9783036515793
Zusätzliche Informationen:	English