Improving multi-crop area assessment through Bootstrapping: A focus on tomato fields

Author:

Şallı BetülORCID,Kavlak BatuhanORCID,Sunar Ayşe Filiz

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geography, Planning and Development

Reference27 articles.

1. Automated detection of diabetic retinopathy using machine learning classifiers;Alabdulwahhab;Eur. Rev. Med. Pharmacol. Sci.,2021

2. Strategic Analysis and Intervention Plan for Fresh and Industrial Tomato in the Agro-Commodities Procurement Zone of the Pilot Integrated Agro-Industrial Park in Central-Eastern Oromia, Ethiopia, Technical Report;Brasesco,2019

3. Random forests;Breiman;Mach. Learn.,2001

4. A bootstrap method for assessing classification accuracy and confidence for agricultural land use mapping in Canada;Champagne;Int. J. Appl. Earth Obs. Geoinf.,2014

5. Derivation of temporal windows for accurate crop discrimination in heterogeneous croplands of Uzbekistan using multitemporal RapidEye images;Conrad;Comput. Electron. Agric.,2014

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