부산대학교 도서관

Faced with aging populations, declining resources, and limited agricultural productivity, ruralareas in South Korea require innovative solutions. This study investigated the potential of drone-basedvegetation indices (VIs) to analyze soybean growth patterns in open-field smart agriculture in Goesan-gun, Chungbuk Province, South Korea. We monitored multi-seasonal normalized difference vegetationindex (NDVI) and the normalized difference red edge (NDRE) data for three soybean lots with differentirrigation methods (subsurface drainage, conventional, subsurface drip irrigation) using drone remotesensing. Combining NDVI (photosynthetically active biomass, PAB) and NDRE (chlorophyll) offered acomprehensive analysis of soybean growth, capturing both overall health and stress responses. Our analysisrevealed distinct growth patterns for each lot. Lot A (subsurface drainage) displayed early vigor and efficientresource utilization (peaking at NDVI 0.971 and NDRE 0.686), likely due to the drainage system. Lot B(conventional cultivation) showed slower growth and potential limitations (peaking at NDVI 0.963 andNDRE 0.681), suggesting resource constraints or stress. Lot C (subsurface drip irrigation) exhibited rapidinitial growth but faced later resource limitations (peaking at NDVI 0.970 and NDRE 0.695). By monitoringNDVI and NDRE variations, farmers can gain valuable insights to optimize resource allocation (reducingcosts and environmental impact), improve crop yield and quality (maximizing yield potential), and addressrural challenges in South Korea. This study demonstrates the promise of drone-based VIs for revitalizingopen-field agriculture, boosting farm income, and attracting young talent, ultimately contributing to amore sustainable and prosperous future for rural communities. Further research integrating additionaldata and investigating physiological mechanisms can lead to even more effective management strategiesand a deeper understanding of VI variations for optimized crop performance.