期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2019
卷号：17
期号：3
页码：1290-1299
DOI：10.12928/telkomnika.v17i3.12234
出版社：Universitas Ahmad Dahlan
摘要：Rice (Oryza Sativa) is the main food for Indonesian people, thus maintaining the stability of rice
production in Indonesia becomes an important issue for further study. A strategy to overcome the issue is
to apply precision agriculture (PA) using remote sensing images as a reference due to its effectiveness.
The initial stage of PA is suitability analysis of rice varieties, including INPARA, INPARI, and INPAGO.
While the representative features that can be extracted from remote sensing images and related to
agriculture field are NDVI, NDWI, NDSI, and BI. Therefore, the aim of this study is to identify the best
model for analyzing the most suitable superior rice varieties using Learning Vector Quantization. The
results show that the best LVQ model is obtained at learning rate value of 0.001, epsilon value of 0.1, and
the features combination of NDWI and BI values (in standard deviation). The architecture generates
accuracy value of 56%.
其他摘要：Rice (Oryza Sativa) is the main food for Indonesian people, thus maintaining the stability of rice production in Indonesia becomes an important issue for further study. A strategy to overcome the issue is to apply precision agriculture (PA) using remote sensing images as a reference due to its effectiveness. The initial stage of PA is suitability analysis of rice varieties, including INPARA, INPARI, and INPAGO. While the representative features that can be extracted from remote sensing images and related to agriculture field are NDVI, NDWI, NDSI, and BI. Therefore, the aim of this study is to identify the best model for analyzing the most suitable superior rice varieties using Learning Vector Quantization. The results show that the best LVQ model is obtained at learning rate value of 0.001, epsilon value of 0.1, and the features combination of NDWI and BI values (in standard deviation). The architecture generates accuracy value of 56%.
关键词：BI;learning vector quantization;NDSI;NDVI;NDWI;suitability analysis of rice varieties
