Open AccessİNSANSIZ HAVA ARACI TEKNOLOJİSİ KULLANILARAK AYÇİÇEĞİ (HELİANTHUS ANNUUS) BİTKİSİNİN KLOROFİL İÇERİKLERİNİN TAHMİN EDİLEBİLİRLİĞİNİN ARAŞTIRILMASI
Author(s) -
Elif Günal,
Orhan Mete KILIÇ,
Şaziye DÖKÜLEN
Publication year - 2021
Publication title -
ejons international journal on mathematic, engineering and natural sciences
Language(s) - English
Resource type - Journals
ISSN - 2602-4136
DOI - 10.38063/ejons.534
Subject(s) - sunflower , helianthus annuus , chlorophyll , chlorophyll a , environmental science , multispectral image , chlorophyll b , vegetation (pathology) , mathematics , horticulture , chemistry , remote sensing , botany , geography , biology , medicine , pathology
High-resolution images obtained with multispectral cameras mounted on unmanned aerial vehicles (UAVs) offer significant potential for agricultural monitoring activities. Chlorophyll content is one of the important parameters in monitoring the growing periods of sunflower plants. In this study, the potential of UAV technology to monitor spatial and temporal changes of sunflower (Helianthus annuus) chlorophyll content was tested. Chlorophyll and vegetation indices calculated using multispectral images taken from the UAV were compared with field chlorophyll measurements using a Spad meter. Significant (p<0.01) high correlations were obtained between the calculated indices and chlorophyll values. The results revealed that the chlorophyll content of sunflower plants can reliably be modeled at p<0.001 significance level using a multivariate linear regression model. The model obtained could successfully explain 28% 2 = 0.28 of the variation in chlorophyll content of sunflowers. The results showed that the models developed between Spad measurement and UAVimages were successful in explaining the variation at a high level of importance. However, although statistically significant, the indices produced in the study could only explained 28% of the variation in chlorophyll content of sunflower plants. Keywords: Sunflower, UAV, Spad meter, Estimation, Vegetation indices