Classification of Rice Growth Phases Using the K-Nearest Neighbor Algorithm in the Irrigation area of Seulimeum Sub District, Aceh, Indonesia

• Published in: IOP conference series. Earth and environmental science Vol. 1297; no. 1; pp. 12006 - 12011
• Main Authors: Maesa, S F, Basri, H, Sugianto, S
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.02.2024
• Subjects: Accuracy; Algorithms; Classification; Irrigation; Phases; Plant growth; Remote sensing; Rice; Rice fields; Satellite imagery; Water demand; Indonesia
• ISSN: 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/1297/1/012006

Abstract Rice ( Oryza sativa L.) is the main food commodity for most of Indonesia’s population. The existence of irrigation areas dramatically affects the water demand in each phase of rice growth. Due to a lack of remote sensing knowledge, classifying the rice growth phase is done manually. This study aims to classify rice growth phases using the K-Nearest Neighbor Algorithm in the irrigation area of the Seulimuem Sub-District. This research uses Google Earth satellite imagery in November 2021 through three stages: multi-resolution segmentation, guided classification, and accuracy testing. The results showed that the paddy and non-paddy fields are 1,425.56 ha and 6,581.70 ha, respectively. The classification results of rice plant growth phases consist of 4 phases, namely the inundation phase, the vegetative phase, the generative phase, and the fallow phase. Based on the accuracy assessment results in November 2021, the overall accuracy ranged from 90.65% to 92.92%, with a kappa index value of 0.85 to 0.87, categorizing it as “almost perfect.”