Correlation between UAV multispectral Imagery and spectroradiometer measurements in sunflower developmental stages

• Alperen Erdoğan Yozgat Bozok University, Sefaatli Vocational School, Architecture and Urban Planning. https://orcid.org/0000-0003-1240-833X
• Ömer Mutluoğlu Konya Technical University, Vocational School of Technical Sciences. https://orcid.org/0000-0002-7846-3713
• Önder Gürsoy Sivas Cumhuriyet University, Geomatics Engineering. https://orcid.org/0000-0002-1531-135X

Keywords: sunflower, spectroradiometer, correlation, multispectral bands, UAV

Abstract

Oilseed crops are among the product groups with a supply deficit in the world. The sunflower oil crisis experienced after 2020 ha increased the importance of sunflower cultivation. The most important stages in agricultural applications are to understand whether the plant is healthy in the early stages before it is formed and to prevent negative results in harvest. With the developing technology, the use of unmanned aerial vehicles (UAVs) and multispectral cameras in agricultural applications has gained enormous importance. Thanks to UAVs, agricultural temporal resolution can be adjusted according to the user's request, and spatial resolution can be adjusted according to the ability of the sensor used and the flight altitude. Spectral resolution is directly proportional to the number of bands and the band wavelength. We performed correlation analysis in this study by comparing the accuracy of the band values with ground measurements made with a spectroradiometer. We measured the sunflower in its vegetative, R-3, and R-5 phases and found that there was a strong correlation (r=0.894) in the green band, r=0.845 in the red, r=0.789 in the red edge (RE) band, and r=0.725 in the near infrared band (NIR). The results show a strong connection between the spectral bands and the spectroradiometer measurements, especially in the green and red bands.

References

Adeleke, B. S., & Babalola, O. O. (2020). Oilseed crop sunflower (Helianthus annuus) as a source of food: Nutritional and health benefits. Food Science & Nutrition, 8(9), 4666–4684. https://doi.org/10.1002/fsn3.1783
Ateşoğlu, A., Kavzoğlu, T., Çölkesen, İ., Özlüsoylu, Ş., Tonbul, H., Yılmaz, E. Ö., & Öztürk, M. Y. (2022). türkiye'de hızlı büyüyen türlere ait spektral kütüphane kurulması: kavak türleri çalışması. Bartın Orman Fakültesi Dergisi, 24(2), 324-338. https://doi.org/10.24011/barofd.1099984
Çölkesen, I., & Yomralioğlu, T. (2014). Arazi örtüsü ve kullanımının haritalanmasında worldview-2 uydu görüntüsü ve yardımcı verilerin kullanımı. Harita Dergisi, 80(152), 12–24. https://www.harita.gov.tr/images/dergi/makaleler/b10d25cbe07aeb3.pdf
Food and Agriculture Organization of the United Nations. (January 10, 2025). The State of Food and Agriculture. https://www.fao.org/faostat/en/#data/QCL
Geipel, J., Link, J., & Claupein, W. (2014). Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system. Remote Sensing, 6(11), 10335–10355. https://doi.org/10.3390/rs61110335
Günal, E., Kılıç O.M., Dökülen Ş. (2021). İnsansız hava aracı teknolojisi kullanılarak ayçiçeği (Helianthus annuus) bitkisinin klorofil içeriklerinin tahmin edilebilirliğinin araştırılması. EJONS International Journal on Mathematic, Engineering and Natural Sciences, 5(19), 691–700. https://doi.org/10.38063/ejons.624
Herbei, M. V., Popescu, C. A., Bertici, R., & Sala, F. (2023). Estımatıon of sunflower crop productıon based on remote sensıng technıques. AgroLife Scientific Journal, 12(1), 87–96. https://doi.org/10.17930/agl2023111
Jafari, R., & Lewis, M. (2012). Arid land characterisation with EO-1 Hyperion hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 19, 298–307. https://doi.org/10.1016/j.jag.2012.06.001
Lee, D.H., Shin, H.S., & Park, J.H. (2020). Developing a p-NDVI map for highland kimchi cabbage using spectral ınformation from UAVs and a field spectral radiometer. Agronomy, 10(11), 1798. https://doi.org/10.3390/agronomy10111798
Li, L., Zheng, X., Zhao, K., Li, X., Meng, Z., & Su, C. (2020). Potential evaluation of high spatial resolution multi-spectral images based on unmanned aerial vehicle in accurate recognition of crop types. Journal of the Indian Society of Remote Sensing, 48(11), 1471–1478. https://doi.org/10.1007/s12524-020-01141-4
Semerci, A., & Durmuş, E. (2021). Analysis of oily sunflower production in Turkey. Turkish Journal of Agriculture-Food Science and Technology, 9(1), 56-62. https://doi.org/10.24925/turjaf.v9i1.56-62.3688
Schneiter, A. A., & Miller, J. F. (1981). Description of sunflower growth stages1. Crop Science, 21(6), 901–903. https://doi.org/10.2135/cropsci1981.0011183x002100060024x
United States Department of Agriculture and Forestry. (February 10, 2024). Oil crops year book. https://www.ers.usda.gov/publications/pub-details?pubid=98237
Wong, W. (2024). What is the custom camera parameters for Mavic 3 Enterprise series and Mavic 3M? https://sdk-forum.dji.net/hc/en-us/articles/12325496609689-What-is-the-custom-camera-parameters-for-Mavic-3-Enterprise-series-and-Mavic-3M
Zeng, C., King, D. J., Richardson, M., & Shan, B. (2017). Fusion of multispectral ımagery and spectrometer data in uav remote sensing. Remote Sensing, 9(7), 696. https://doi.org/10.3390/rs9070696