Assessment of a Contamination of Crops of Sunflower by Means of Unmanned Aerial Vehicles

Authors

  • А. Б. Ачасов V. N. Karazin Kharkiv National University V. V. Dokuchaev Kharkiv National Agrarian University, Ukraine https://orcid.org/0000-0002-5009-7184
  • А. О. Седов V. V. Dokuchaev Kharkiv National Agrarian University, Ukraine
  • А. О. Ачасова V. V. Dokuchaev Kharkiv National Agrarian University, Ukraine

Keywords:

UAV, drone, monitoring of crops, weeds, sunflower, ragweed polynnolisty, harvest, decryptions, supervised classification,

Abstract

 Purpose.  Evaluate the use kvadrokopteriv for evaluation of weed-infested crops of sunflower. Methods. Aerial survey using drones, object-oriented image analysis.  Results. In the article are given the results of assessment of a contamination of crops of sunflower by results of decryption of the pictures made by means of the UAV in the visible range.It is shown that the best results of decoding of photo-images are received when using supervised classification by a method of the maximum plausibility. Conclusions. For improving of recognition of weeds and separation of their image from images of cultural plants it is expedient to use the object-oriented analysis.

Author Biographies

А. Б. Ачасов, V. N. Karazin Kharkiv National University V. V. Dokuchaev Kharkiv National Agrarian University

д-р с.-г. наук, доц.

А. О. Седов, V. V. Dokuchaev Kharkiv National Agrarian University

Харьковский национальный аграрный университет имени В. В. Докучаева

А. О. Ачасова, V. V. Dokuchaev Kharkiv National Agrarian University

канд. біол. наук, доц.

References

Global Market for Commercial Applications of Drone Technology Valued at over $127 bn. http://press.pwc.com/News-releases/global-market-for-commercial-applications-of-drone-technology-valued-at-over--127-bn/s/AC04349E-C40D-4767-9F92-A4D219860CD2

Гудзь В.П., Примак І.Д., Будьонний Ю.В., Танчик С.П. Землеробство Підручник. 2-ге вид. перероб. та доп. / За ред. В.П. Гудзя. - К.: Центр учбової літератури, 2010. - 464 с.

Шпанев А. М. Новые подходы к методике учета сорных растений / А. М. Шпанев, П. В. Лекомцев // Защита и карантин растений: ежемесячный журнал для специалистов, ученых и практиков. - 2012. - N 8. - С. 38-41

Архипова О.Е., Качалина Н.А., Тютюнов Ю.В, Ковалев О.В. Оценка засоренности антропогенных фитоценозов на основе данных дистанционного зондирования Земли (на примере амброзии полыннолистной). Исследования Земли из космоса, 2014. № 6. C. 15-26.

De Castro, A.I.; Lopez Granados, F.; Jurado-Exposito, M. Broad-scale cruciferous weed patch classification in winter wheat using QuickBird imagery for in-season site-specific control—Springer. Precis. Agric. 2013, 14, 392–413.

Hunt, E.R., Jr.; Hively, W.D.; Fujikawa, S.J.; Linden, D.S.; Daughtry, C.S. T.; McCarty, G.W. Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring. Remote Sens. 2010, 2, 290–305.

López-Granados, F. Weed detection for site-specific weed management: Mapping and real-time approaches. Weed Res. 2011, 51, 1–11

Peña J.M., J. Torres-Sánchez, A. Serrano, A.I. de Castro, F. López-Granados. 2015. Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors, 15(3), 5609-5626

Зуза В. С. Нова концепція рівня забур’яненості посівів сільськогосподарських культур при гербологічному моніторингу / В. С. Зуза // Вісн. ХНАУ. Сер. «Ґрунтознавство, агрохімія, землеробство, лісове господарство»: зб. наук. пр. – Х.: ХНАУ ім. В. В. Докучаєва, 2011. – № 1. – С. 169–173.

Yu, Q.; Gong, P.; Clinton, N.; Biging, G.; Kelly, M.; Schirokauer, D. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogramm. Eng. Remote Sens. 2006, 72, 799–811.

Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens.2010, 65, 2–16.

Pena, J.M.; Torres-Sanchez, J.; de Castro, A.I.; Kelly, M.; Lopez-Granados, F. Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images. PLoS One 2013, 8, e77151.

Issue

Section

Modern Geographic and Ecological Environment Research