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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

SiRCub, A Novel Approach to Recognize Agricultural Crops Using Supervised Classification

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Author(s):
Tomas, Jordi Creus [1] ; Faria, Fabio Augusto [2] ; Dalla Mora Esquerdo, Julio Cesar [3] ; Coutinho, Alexandre Camargo [3] ; Medeiros, Claudia Bauzer [4, 5]
Total Authors: 5
Affiliation:
[1] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil
[2] Univ Fed Sao Paulo, Inst Sci & Technol, Sao Jose Dos Campos - Brazil
[3] Brazilian Agr Res Corp, Embrapa Agr Informat, Campinas, SP - Brazil
[4] Univ Campinas UNICAMP, Inst Comp, Campinas, SP - Brazil
[5] Paris Dauphine Univ, Paris - France
Total Affiliations: 5
Document type: Journal article
Source: INTERNATIONAL JOURNAL OF AGRICULTURAL AND ENVIRONMENTAL INFORMATION SYSTEMS; v. 8, n. 4, p. 20-36, OCT-DEC 2017.
Web of Science Citations: 0
Abstract

This 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. (AU)

FAPESP's process: 13/08293-7 - CCES - Center for Computational Engineering and Sciences
Grantee:Munir Salomao Skaf
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 12/25169-5 - Spatial and temporal multi-level overlapping summaries for pattern detection, with application in agriculture
Grantee:Jordi Creus Tomàs
Support type: Scholarships in Brazil - Post-Doctorate