UAV기반 식생지수 활용을 통한 질소비료처리에 따른 밀 단백질 함량 및 수확량 추정

• Published in: Korean journal of crop science Vol. 69; no. 4; pp. 264 - 272
• Main Authors: 정현진, Hyun-jin Jung, 전다솜, Dasom Jeon, 이경도, Kyung-do Lee, 류재현, Jae-hyun Ryu, 안호용, Ho-yong Ahn, 전영아, Young-ah Jeon, 김숙경, Sook-gyeong Kim, 정한용, Han-yong Jeong
• Format: Journal Article
• Language: Korean
• Published: 한국작물학회 31.12.2024
• Subjects: nitrogen; remote sensing; UAV; vegetation index; wheat; 농학; vegetation index; remote sensing; wheat; UAV; nitrogen
• ISSN: 0252-9777; 2287-8432
• DOI: 10.7740/kjcs.2024.69.4.264

Remote Sensing utilizes unmanned aerial vehicles (UAVs), such as drones, to observe and evaluate the condition and growth environment of crops through close monitoring during low-altitude flights in agriculture. This study was conducted to estimate the wheat yield and protein content of hard wheat varieties in Korea by analyzing the correlation between the yield and protein content of each treatment plot and the following UAV image-based vegetation indices : Normalized Difference Vegetation Index, Green Normalized Difference Vegetation Index, Ratio Vegetation Index, Green Ratio Vegetation Index, and Normalized Difference Red Edge (NDRE). Among UAV image-based vegetation indices, the NDRE at the ripening stage showed a correlation coefficient of 0.71-0.86 with the protein content, although there was no relationship between wheat yield and vegetation indices. Based on these results, a distribution map of the wheat protein content was generated. Using UAV-based vegetation indices to estimate protein content and produce spatial distribution information can improve wheat quality and assist in decision-making in agricultural fields.