Suitability analysis of rice varieties using learning vector quantization and remote sensing images

[...]there are other advantages of RS image in PA, namely remote sensing images are capable to present the earth surface in the form of the existence of the objects without the need to make direct contact with the objects, up-to-date, as well as reliable. [...]the aim of this study is to propose a novel PA technology for the pre-planting stage, specifically, a method for identifying the suitable rice variety for an area using remote sensing images as the reference. Some of the features of RS image that can be used to calculate the value of those factors are the Normalized Difference Vegetation Index (NDVI), Normalized Difference Salinity Index (NDSI), Normalized Difference Water Index (NDWI), and Brightness Index (BI). [...]determining the combination of these features in LVQ-based classification model becomes essential for this study, hence we can determine the suitability of superior rice varieties for an area.Therefore, the aim of this study is to implement the LVQ for identifying the land suitability for several rice varieties by using remote sensing images as reference and some features such as NDVI, NDSI, NDWI, and BI in particular. 2.Research Method We implement a popular method for classification task in neural networks (NN), namely Learning Vector Quantization (LVQ). W for wetness is to show the interrelationship of soil and canopy moisture [23]. [...]plant growth is strongly influenced by soil moisture, so it can be said that soil moisture can be used as an indicator of land management. Since each of the extracted features in the form of matrix with size is equal to image size, then we compute mean value and standard deviation value for each feature and each image.