MONITORING VEGETATION HARVEST OF COFFEE TREES USING KNN-CLUSTERING ALGORITHM - Dwi Handoko; Nizamiyati Nizamiyati; Herlini Oktaria; Agus Mulyanto; Muhamad Brilliant (Department of Computer Engineering, Universitas Wiralodra, 2023)
Coffee is one of the plantation commodities spread throughout Indonesia. Coffee is the main commodity for export in Tanggamus Regency. The prediction of crop yields based on aerial photography is the main problem in this study, then there is no dataset of aerial imagery of coffee plantations that are specifically used for the purpose of determining coffee tree vegetation on coffee plantations so that farmers can find out which land is still overgrown by other plants. in addition to coffee trees and the possibility of making predictions for crop yields from aerial imagery of the coffee plantations, this research is also another urgency. This study is intended to build an intelligent model to detect the amount of coffee tree vegetation in a plantation using the KNN-Clustering segmentation algorithm. The image of the coffee tree was taken using a drone with a height of 50 m and an area of 0.25 ha. Preprocessing was carried out. The preprocessed image is called a dataset. After that, the segmentation process is carried out using the Region Growing method to form a black and white image. After Region Growing is done, then the image in Clustering uses the KNN-Clustering method to determine the color pattern of the image in the coffee plantation to distinguish the types of vegetation in the coffee plantation. From the results of KNN-Clustering, the area of coffee tree vegetation is obtained from a total of 0.25 ha of coffee plantation images.