Abstract
The use of high-resolution remote sensing image data is necessary to distinguish essential plants with other plants. This study uses image data taken using Unmanned Aerial Vehicle (UAV) to identify essential plants especially citronella and kaffir lime. To distinguish the structure of essential plants with other objects used texture features extracted by wavelet daubechies method. The features that have been ekstract, then is grouped based on the proximity feature with the Self Organizing Map (SOM) Neural Network. Thus, objects that have similar features will clump together. The tests were conducted on two groups of data sets, where the first group data consisted of plants, buildings and vacant lots, while the second group data consisted only of plants. The results of testing of the first data group shows that the techniques can recognize the citronella plants among other objects, especially building objects and bare land with purity of 0.862745 and Silhouette Coeficient of 0.5520671. While in the second data group, the value of purity and Silhouette Coeficient decreased to 0.737705 and 0.161028. However, from the test of the second data group still shows that the method used can distinguish citronella crops to other plants.
Highlights
Essential plants have a high commercial value because it produces essential oils that are widely used in various industries such as perfumes, cosmetics and medicines
Of the 200 varieties of essential plants, Indonesia has the potential of 40 types of plants and 15 species of which produce essential oils that become export commodities
Essential plants spread almost in all regions in Indonesia, but it’s difficult to identify the existence of this plant because the data collection has not been managed properly and difficult to validate the extent of existing plants
Summary
Essential plants have a high commercial value because it produces essential oils that are widely used in various industries such as perfumes, cosmetics and medicines. This study shows that medium resolution satellite data can produce high accuracy. Research conducted by Lu and He (2017) shows that UAV image can be used to distinguish grassland species in a particular area Based on these advantages, our study used UAV image to identify essesntial plants, especially for citronella and kaffir lime. Essential plants can be distinguished from buildings and bare land using color features. This feature is less effective when used to distinguish essential plants with other plants such as rice, corn and trees. Research conducted by Bakhshipour et al (2017) shows that feature extraction with wavelet can improve the effectiveness of weed detection processes in bit plants. While the accuracy produced in the identification of disease on Ethiopian Coffee plant is 90.07% (Mengistu et al, 2016), and about 90% accuracy on plant disease (Patil and Kumar, 2011)
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