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Document Type : Latin Dissertation
Language of Document : English
Record Number : 153837
Doc. No : ET25629
Main Entry : Yoji Uno
Title Proper : Application of Machine Learning Methods and Airborne Hyperspectral Remote Sensing for Crop Yield Estimation.POMPES PAR TRANSITIONS MULTIPLES
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : Accurate description of within-field crop yield variability is one of the greatest concernsin precision agriculture. This study investigated the potential of developing in-seasoncrop yield forecasting and mapping systems based on interpretation of airbornehyperspectral remote sensing imagery by machine learning algorithms. The data used forthis study was obtained over a corn (Zea mays L.) field in eastern Canada..
Subject : Electericl tess
: برق
electronic file name : TL49894.pdf
Title and statement of responsibility and : Application of Machine Learning Methods and Airborne Hyperspectral Remote Sensing for Crop Yield Estimation.POMPES PAR TRANSITIONS MULTIPLES [Thesis]
 
 
 
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