Identifying Citronella Plants From UAV Imagery Using Support Vector Machine
Candra Dewi, Achmad Basuki
High-resolution imagery taken from Unmanned Aerial Vehicle (UAV) is now often used as an alternative in monitoring the agronomic plants compared to satellite imagery. This paper presents a method to identify Citronella among other plants based on UAV imagery. The method utilizes Support Vector Machine (SVM) to classify Citronella among other plants according to the extraction of texture feature. The implementation of the method was evaluated using two group of datasets: 1) consists of Citronella, Kaffir Lime, other green plants, vacant soil, and buildings, and 2) consists of Citronella and paddy rice plants. The evaluation results show that the proposed method can identify Citronella on the first group of datasets with an accuracy 94.23% and Kappa value 88.48%, whereas on the second group of datasets with an accuracy 100% and Kappa value 100%.