Agriculture as an element of ensuring food security of countries plays an important
role in the world and regional economy. The use of digital technologies in agriculture can be
one of the sources of growth in the industry. Data in agriculture can be collected using
various mechanisms, including the use of unmanned aerial vehicles. This article discusses the
selection of unmanned aerial vehicles for agriculture and gives recommendations to farmers
on the choice of drones for data collection and monitoring. For example, it is possible to
collect useful information on the state of agricultural fields (the size of fields, the level of
germination, the state of vegetation) with the help of drones. The basis of this study is an
overview of the relevant literature on the use of unmanned aerial vehicles in agriculture in
different countries and the author's own experience in implementing the project with the use
of an agricultural drone in the experimental fields of the Astrakhan State University. In
addition, a survey of farmers of the Astrakhan region was conducted to identify the most
demanded areas of agriculture for monitoring in order to increase the effectiveness of
decision-making.
1. Lelong C. C. D. et al. Assessment of unmanned aerial vehicles imagery
for quantitative monitoring of wheat crop in small plots //Sensors. – 2008.
– Vol. 8. – № 5. – pp. 3557-3585.
2. Berni J. A. J. et al. Thermal and narrowband multispectral remote sensing
for vegetation monitoring from an unmanned aerial vehicle //IEEE
Transactions on Geoscience and Remote Sensing. – 2009. – Vol. 47. – №
3. – pp. 722-738.
3. d'Oleire-Oltmanns S. et al. Unmanned aerial vehicle (UAV) for
monitoring soil erosion in Morocco //Remote Sensing. – 2012. – Vol. 4. –
№ 11. – pp. 3390-3416.
4. Everaerts J. et al. The use of unmanned aerial vehicles (UAVs) for remote
sensing and mapping //The International Archives of the Photogrammetry,
Remote Sensing and Spatial Information Sciences. – 2008. – Vol. 37. –
pp. 1187-1192.
5. Zainuddin K. et al. Verification test on ability to use low-cost UAV for
quantifying tree height //Signal Processing & Its Applications (CSPA),
2016 IEEE 12th International Colloquium. – IEEE, 2016. – pp. 317-321.
6. Park J. K., Park J. Crop classification using imagery of drone
//International Conference on Environmental Engineering and Remote
Sensing (EERS2015). – 2015. - pp. 91-94.
7. Teke M., Yardımcı Y. Classification of crops using multitemporal
hyperion images //Agro-Geoinformatics (Agro-geoinformatics), 2015
Fourth International Conference. – IEEE, 2015. – pp. 282-287.
8. Bellvert J. et al. Mapping crop water stress index in a ‘Pinotnoir’vineyard: comparing ground measurements with thermal remote
sensing imagery from an unmanned aerial vehicle //Precision agriculture.
– 2014. – Vol. 15. – № 4. – pp. 361-376.
9. Gago J. et al. UAVs challenge to assess water stress for sustainable
agriculture //Agricultural water management. – 2015. – Vol. 153. – pp. 9-
19.
10. Yue J. et al. The application of unmanned aerial vehicle remote sensing in
quickly monitoring crop pests //Intelligent Automation & Soft
Computing. – 2012. – Vol. 18. – № 8. – pp. 1043-1052.
11. Faiçal B. S. et al. The use of unmanned aerial vehicles and wireless sensor
networks for spraying pesticides //Journal of Systems Architecture. –
2014. – Vol. 60. – № 4. – pp. 393-404.
12. Zarco-Tejada P. J. et al. Estimating leaf carotenoid content in vineyards
using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV) //Agricultural and forest meteorology. – 2013. –
Vol. 171. – pp. 281-294.
13. Córcoles J. I. et al. Estimation of leaf area index in onion (Allium cepa L.)
using an unmanned aerial vehicle //Biosystems engineering. – 2013. –
Vol. 115. – № 1. – pp. 31-42.
14. Göktoǧan A. H. et al. A rotary-wing unmanned air vehicle for aquatic
weed surveillance and management //Journal of Intelligent and Robotic
Systems. – 2010. – Vol. 57. – p. 467.
15. Torres-Sánchez J. et al. Configuration and specifications of an unmanned
aerial vehicle (UAV) for early site specific weed management //PloS one.
– 2013. – Vol. 8. – № 3. – pp. 1-15.
16. Zhang C., Kovacs J. M. The application of small unmanned aerial systems
for precision agriculture: a review //Precision agriculture. – 2012. – Vol.
13. – № 6. – pp. 693-712.
17. Forster D., Buehler Y., Kellenberger T. Mapping urban and peri-urban
agriculture using high spatial resolution satellite data //Journal of Applied
Remote Sensing. – 2009. – Vol. 3. – № 1. – pp. 1-12.
18. Schmedtmann J., Campagnolo M. L. Reliable crop identification with
satellite imagery in the context of common agriculture policy subsidy
control //Remote Sensing. – 2015. – Vol. 7. – № 7. – pp. 9325-9346.
19. Ipate G., Voicu G., Dinu I. Research on The Use of Drones in Precision
Agriculture //University Politehnica of Bucharest Bulletin Series. – 2015.
– Vol. 77. – № 4. – pp. 1-12.
20. Marinello F. et al. Technical analysis of unmanned aerial vehicles
(drones) for agricultural applications //Engineering for Rural
Development. – 2016. – Vol. 15. - pp. 870-875.
21. Bhandari A. K., Kumar A., Singh G. K. Feature extraction using
Normalized Difference Vegetation Index (NDVI): a case study of
Jabalpur city //Procedia Technology. – 2012. – Vol. 6. – pp. 612-621.
Koshkarov Alexander Vasilievich
Candidate of Technical Sciences
Email: avkoshkarov@gmail.com
Astrakhan State University
Astrakhan, Russian Federation
