Methodological Basis of The UAVs Use for the Weed Detection

  • A. B. Achasov Харківський національний університет імені В.Н. Каразіна http://orcid.org/0000-0002-5009-7184
  • A. O. Sedov V. V. Dokuchaev Kharkiv National Agrarian University
  • A. O. Achasova National Scientific Center «Institute for soil science and agrochemistry research named after A.N. Sokolovsky»
Keywords: UAV, drone, crop monitoring, weed, sunflower, decryption of images, controlled classification

Abstract

Purpose. To work out methodological approaches to the use of quadcopters for weeds assesment. Methods. The shooting was carried out using DJI Phantom Vision 2+ and LadyBug Copper Dot. The LadyBug was shoted in the visible and near-infrared range using the 12-megapixel S100 NDVI UAV-Kit camera with elevations: 20 m, 40 m and 60 m. The DJI Phantom Vision 2+ was shot in the visible range of the GoPro 14 megapixel camera altitudes: 10 m, 15 m, 30 m and 60 m. Decryption of photographs was carried out using the controlled classification method in QGIS and TNTmips programs. Weed accounting was performed on control sites 1m2 by weight method, taking into account their qualitative composition. Results. It is shown that the best results of weed recognition during decoding of images was obtained by the use of controlled classification according to the maximum likelihood method under conditions of shooting from heights up to 40 m. In order to improve the recognition of weeds and separate their image from images of cultivated plants, it is expedient to use the object-oriented analysis. At the stage of sunflower budding, about 30% of the weeds are closed from the remote observation, which led to an automatic underestimation of number of weeds. Conclusions. In order to evaluate the crop contamination, it is possible to successfully use the data from UAVs in a visible range of electromagnetic waves under low altitudes (up to 40 meters) and the use of a controlled classification method for decoding images. For the recognition of weeds, the images in the infrared range do not have advantages over images in the visible range. It is necessary to additionally apply ground-based control of weeds to assess the proportion of "hidden" from remote observation of weeds.

Downloads

Download data is not yet available.

Author Biographies

A. B. Achasov, Харківський національний університет імені В.Н. Каразіна

дрктор сільськогосподарських наук

A. O. Achasova, National Scientific Center «Institute for soil science and agrochemistry research named after A.N. Sokolovsky»

кандидат біологічних наук, доцент

References

Achasov, A. B., Achasova, A. O., Titenko, G. V., Seliverstov, O. Yu., Syedov, A. O. (2015) Shhodo vy`kory`stannya BPLA dlya ocinky` stanu posiviv [UAV usage for crop estimation]. Visnyk of V.N. Karazin Kharkiv national university Series “Ecology”, 13, 13-18. [In Ukrainian]

Savin, I.YU., Vernyuk, YU.I., Faraslis, I. (2015) Vozmozhnosti ispol'zovaniya bespilotnyh letatel'nyh apparatov dlya monitoringa produktivnosti pochv [Possibilities of using unmanned aerial vehicles for monitoring of soil productivity]. Bulletin of Soil Institute named V.V. Dokuchaev, 80, 95-106. [In Russian]

Pfeifer, J., Khanna, R., Dragos, C., Popovic, M., Galceran, E., Kirchgessner, N., Walter, A., Siegwart, R., Liebisch, F.(2016). Towards automatic UAV data interpretation for precision farming. Proc. ofthe International Conf. ofAgricultural Engineering (CIGR)

Tokekar, P., Hook, J. V., Mulla, D., Isler, V.( 2013). Sensor planning for a symbiotic UAV and UGV system for precision agriculture, 5321-5326.

Achasov, A. B., Syedov, A. O., Achasova, A. O.(2016) Ocinka zabur'yanenosti posiviv sonyashny`ka za dopomogoyu bezpilotny`x lital`ny`x aparativ [Assessment of a contamination of crops of sunflower by means of unmanned aerial vehicles]. Man and the Environment. Issues of Neoecology, 3-4, 69-74. [In Ukrainian]

Shpanev, A. M., Lekomcev, P. V. (2012) Novye podhody k metodike ucheta sornyh rastenij [New approaches to the method of accounting for weed plants]. Plant protection and quarantine: a monthly journal for specialists, scientists and practitioners, 8, 38-41. [In Russian]

Guerrero, J. M., Pajares, G., Montalvo, M., Romeo, J., Guijarro, M. (2012). Support vector machines for crop/weeds identification in maize fields. Expert Systems with Applications, 39(12):11149 – 11155.

Guo, W., Rage, U. K., Ninomiya, S. (2013). Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model. Computers and Electronics in Agriculture, 96:58– 66.

Hamuda, E., Glavin, M., Jones, E.(2016). A survey of im processing techniques for plant extraction and segmentation in the field. Computers and Electronics in Agriculture, 125:184–199.

Lottes, P., Hoferlin, M., Sander, S., Muter, M., Schulze-Lammers, P., Stachniss, C.(2016). An effective classification system for separating sugar beets and weeds for precision farming applications. In Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA).

Nekos, A. N., Achasov, A. B., Kochanov, E. O. (2017) Metody` vy`miryuvannya parametriv navko-ly`shn`ogo seredovy`shha: dy`stancijni metody: pidruchny`k. [Methods of measuring environmental parameters: distance learning methods] Kharkiv, 2017, 244. [In Ukrainian]

Aryeshnikova, B.A. (1992). Zaxy`st zernovy`x kul`tur vid populyaciyi shkidny`kiv, xvorob ta bur'yaniv pry` intensy`vny`x texnologiyax [Protection of grain crops from the population of pests, diseases and weeds in intensive technologies]. Kiyv. Urojai, 224. [In Ukrainian]

Koot, Th. M. Weed detection with Unmanned Aerial Vehicles in agricultural systems. Thesis Report GIRS-2014-37. - Centre for Geo-Information. Wageningen University. Available at: http://edepot.wur.nl/333537

Published
2018-05-11
How to Cite
Achasov, A. B., Sedov, A. O., & Achasova, A. O. (2018). Methodological Basis of The UAVs Use for the Weed Detection. Man and Environment. Issues of Neoecology, (1-2(29), 21-28. https://doi.org/10.26565/1992-4224-2018-29-02
Section
Modern Geographic and Ecological Environment Research

Most read articles by the same author(s)