Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Júnior, Clóvis Cechim; Shemmer, Rosangela Carline; Johann, Jerry Adriani; de Almeida Pereira, Gabriel Henrique; Deppe, Flávio; Opazo, Miguel Angel Uribe; Silva Junior, Carlos Antonio da
• Is Part Of: Canadian journal of remote sensing. Volume 45:Issue 1(2019)
• Page Start: 16
• Page End: 25
• Publication Date: 2019-01-02
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2019.1594734
• Languages: English
• ISSNs: 0703-8992

Abstract

Abstract: The objective of this research was to distinguish and estimate cultivated areas of soybean and corn in Paraná State, Brazil, in the 2014/2015 crop season. The main obstacle in mapping summer crops using vegetation index images is to separate the cultivated areas with soybean and corn. These crops planted in a similar period present similar spectral signatures. Thus, with the use of Data Mining techniques (DM) and Artificial Neural Network (ANN) it was possible to carry out the crop mapping, even for those that present similarities in spectral-temporal profile of vegetation indexes. The accuracy obtained in the mappings resulted in a KI (Kappa Index) of 0.78 and 89% of OA (overall accuracy) indicating a high accuracy in the separation of soybean and corn summer crops.