Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach
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 pl...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-01-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2019.1594734 |
| _version_ | 1797661186218524672 |
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| author | Clóvis Cechim Júnior Rosangela Carline Shemmer Jerry Adriani Johann Gabriel Henrique de Almeida Pereira Flávio Deppe Miguel Angel Uribe Opazo Carlos Antonio da Silva Junior |
| author_facet | Clóvis Cechim Júnior Rosangela Carline Shemmer Jerry Adriani Johann Gabriel Henrique de Almeida Pereira Flávio Deppe Miguel Angel Uribe Opazo Carlos Antonio da Silva Junior |
| author_sort | Clóvis Cechim Júnior |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T18:40:59Z |
| format | Article |
| id | doaj.art-5bb7a3dc0a4b41719fceccc380f4995d |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:40:59Z |
| publishDate | 2019-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-5bb7a3dc0a4b41719fceccc380f4995d2023-10-12T13:36:22ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712019-01-01451162510.1080/07038992.2019.15947341594734Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New ApproachClóvis Cechim Júnior0Rosangela Carline Shemmer1Jerry Adriani Johann2Gabriel Henrique de Almeida Pereira3Flávio Deppe4Miguel Angel Uribe Opazo5Carlos Antonio da Silva Junior6State University of West Paraná (UNIOESTE)State University of West Paraná (UNIOESTE)State University of West Paraná (UNIOESTE)Meteorological System of Paraná (SIMEPAR)Meteorological System of Paraná (SIMEPAR)State University of West Paraná (UNIOESTE)State University of Mato Grosso (UNEMAT)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.http://dx.doi.org/10.1080/07038992.2019.1594734 |
| spellingShingle | Clóvis Cechim Júnior Rosangela Carline Shemmer Jerry Adriani Johann Gabriel Henrique de Almeida Pereira Flávio Deppe Miguel Angel Uribe Opazo Carlos Antonio da Silva Junior Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach Canadian Journal of Remote Sensing |
| title | Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach |
| title_full | Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach |
| title_fullStr | Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach |
| title_full_unstemmed | Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach |
| title_short | Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach |
| title_sort | artificial neural networks and data mining techniques for summer crop discrimination a new approach |
| url | http://dx.doi.org/10.1080/07038992.2019.1594734 |
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