02377naa a2200397 a 450000100080000000500110000800800410001902400420006010000180010224501170012026000090023752010410024665000280128765000150131565000130133065000430134365000190138665000250140565000210143065000250145165000170147665300280149365300240152165300300154565300270157565300090160265300210161165300220163265300110165465300270166570000170169270000260170970000200173570000200175577302040177521777642025-08-07       2019    bl uuuu     u00u1 u    #d7 a10.4018/978-1-5225-7033-2.ch050.2DOI1 aTOMÀS, J. C.  aSiRCubba novel approach to recognize agricultural crops using supervised classification.h[electronic resource]  c2019  aThis paper presents a new approach to deal with agricultural crop recognition using SVM (Support Vector Machine), applied to time series of NDVI images. The presented method can be divided into two steps. First, the Timesat software package is used to extract a set of crop features from the NDVI time series. These features serve as descriptors that characterize each NDVI vegetation curve, i.e., the period comprised between sowing and harvesting dates. Then, it is used an SVM to learn the patterns that define each type of crop, and create a crop model that allows classifying new series. The authors present a set of experiments that show the effectiveness of this technique. They evaluated their algorithm with a collection of more than 3000 time series from the Brazilian State of Mato Grosso spanning 4 years (2009-2013). Such time series were annotated in the field by specialists from Embrapa (Brazilian Agricultural Research Corporation). This methodology is generic, and can be adapted to distinct regions and crop profiles.  aArtificial intelligence  aLand cover  aLand use  aNormalized difference vegetation index  aRemote sensing  aTime series analysis  aVegetation index  aSensoriamento Remoto  aUso da Terra  aAprendizado de máquina  aCrop classification  aEnhanced Vegetation Index  aÍndice de vegetação  aLULC  aMachine Learning  aSéries temporais  aSIRCub  aSupport Vector Machine1 aFARIA, F. A.1 aESQUERDO, J. C. D. M.1 aCOUTINHO, A. C.1 aMEDEIROS, C. B.  tIn: INFORMATION RESOURCES MANAGEMENT ASSOCIATION (ed.). Environmental information systems: concepts, methodologies, tools, and applications. Hershey: IGI Global, 2019.gv. II, chap. 50, p. 1129-1147.