Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach

Abstract

The title and author names are listed on this sheet as they will be published, both on your paper and on the Table of Contents. Please review and ensure the information is correct and advise us if any changes need to be made. In addition, please review your paper as a whole for typographical and essential corrections.Your PDF proof has been enabled so that you can comment on the proof directly using Adobe Acrobat. For further information on marking corrections using Acrobat, please visit http://journalauthors.tandf.co.uk/production/acrobat.asp; https://authorservices.taylorandfrancis.com/how-to-correct-proofs-with-adobe/ The CrossRef database (www.crossref.org/) has been used to validate the references. AUTHOR QUERIES Q1'This procedure decomposed the annual variation of the EVI and are represented by the mean (H0), by the phase that indicates the cycle of the culture in intervals of 0 -360 , and by the Amplitude (H1) that indicates the maximum vegetation variation for the entire period.' The part of this sentence in bold is unclear. Could author please clarify and reword. Q2There is no mention of (Bouckaert et al. 2010, IBGE -Instituto Brasileiro de Geografia e Estat ıstica 2016) in the text. Please insert a citation in the text or delete the reference as appropriate. Q3Please provide the publisher location "Coutinho et al. 2012". Q4Please provide the publisher location for "K€ oppen and Geiger 1928".

Investigation on the use of ensemble learning and big data in crop identification

“… (15 bands) - > OA (90%). [ 39 ] ANN and PCA TERRA/AQUA-Modis and Landsat-OLI OA (89%) [ 40 ] SVM and RF Unmanned Aerial Vehicle (UAV) images SVM achieved the best crop classification based only on spectral information. [ 41 ] Maximum Likelihood and Minimum Distance Spot-5 images [ 42 ] Polarimetric Correlation Coefficients.…”