Keywords: precision agriculture, quadcopter, RGB images, variety wheat


In June 2019, a drone image recording was performed equipped with an RGB camera on an experimental wheat field at an interval of seven days three times a day. Within one day (12.06.2019) information was collected about the weather conditions and eight measurements were made every hour. The commonly accepted indices were calculated: VARIgreen and ExG for the one-month and one-day observations of the received RGB digitized information. A regression model was created for a full factor experiment of type 23. The interaction of the factors that characterize the conditions of the video was evaluated. Conclusions were formulated as to the influence of the factors under which the observations were made. A recommendation was made with regard to which part of the day it is most appropriate to take photos and video surveillance of wheat.


Бисерков, В., (2017), Нови технологични възможности за екосистемни анализи в България, сп. "Наука", кн. 5, том XXXVII, изд. Съюз на учените в България, стр. 16- 22. ISBN 0861 3362.
Link Spisanie Nauka

Митков, А., (2011), Теория на експеримента, изд. "Дунавпрес", Русе

Chamurliyski, P., 2019. Historical aspects and achievements of the bread wheat (Triticum aestivum L.) in Southern Dobrudzha, New Knowledge Journal of Science, 8 (2): 60-70  
Google Scholar

Ekielski A., Koronczok J., Lorencki J., Czech T., & Tulska E., (2017), Crops diagnosis using Hurst exponent values in field image analysis, DOI: 10.24326/fmtmsa.2017.19, 101-108;

Gitelson, A. A., Kaufman Y. J., Yoram J.; Stark Robert, & Rundquist, D., (2002), Novel Algorithms for Remote Estimation of Vegetation, Fraction, Papers in Natural Resources. 149. http://digitalcommons.unl.edu/natrespapers/149;

Jackson R. D. (1983), Spectral Indices in n-Space, Рemote sensing of environment no. 13:409-421,

Laliberte, A. S., & Rango A., (2009), Texture and Scale in Object-Based Analysis of Subdecimeter Resolution Unmanned Aerial Vehicle (UAV) Imagery, IEEE transactions on geoscience and remote sensing, vol. 47, no. 3, march 2009;

Rao Mogili U. M., & Deepak B. B. V. L, (2018), Review of application of drone system in precision agriculture, Procedia Computer Science 133 502-509;

Torres-Sanches J., Pena Baragan J. M., Gome-Candom D., De Castro A. I., & Lopes Granados F., (2012) Imagery from unmanned aerial vehicle for early site-specific weed management, Precision agricultures'13, 193-199
Google Scholar

Van der Wal T., Abma B., Viguria A., Previnaire E., Zarco-Tejada P. J., Serruys P., van Valkengoed E., & van der voet P., (2013), Field copter: unmanned aerial system for crop monitoring services, Precision agricultures'13, 169-175

Woebbecke DM, Meyer GE, Von Bargen K, & Mortensen DA. (1995), Color indices for weed identification under various soil, residue, and lighting conditions. Trans. Am. Soc. Agric. Eng., 38: 259-269.

Total number of hits on abstract = 318 times

Downloads for 2021

Download data is not yet available.
How to Cite
Mihajlow, R., & Ivanova, A. (2019, December 31). DRONE VIDEO CAPTURE – A NEW METHOD IN PRECISION AGRICULTURE. ANNUAL JOURNAL OF TECHNICAL UNIVERSITY OF VARNA, BULGARIA, 3(2), 32-38. https://doi.org/10.29114/ajtuv.vol3.iss2.141
Bookmark and Share