Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 3, pp. 72-83

Methods for detection of weeds, pests and diseases of plants from remote sensing data

I.M. Mikhailenko 1 , I.V. Voronkov 2 
1 Agrophysical Research Institute, Saint-Petersburg, Russia
2 JGSC "Engineering Center"GEOMIR", Mytischi, Moscow region, Russia
Accepted: 05.04.2016
DOI: 10.21046/2070-7401-2016-13-3-72-83
A method for a crop state evaluation on the basis of space remote sensing data and vegetation indices is discussed. The method essentially reduces the informativeness of the spectral channels as the vegetation indices represent convolution of signals of individual channels. Besides, both a stochastic character of the problem and an initially high uncertainty do not allow solving the problem of weed detection, as well as detection of plant diseases and pests using vegetation indices. To solve the problem, we propose to use a set of spectral channels with the highest informativeness. The selection of the channels is based on the Shannon theory of information. The problem of weed, plant disease and pest detection can be solved in two steps. The first step includes the use of the information from all the spectral channels and satellite remote sensors to develop a classification for such groups as “weeds”, “diseases” and “pests”. To fulfill the first step a statistical approach is used with an assumption that the reflectance parameters of the sensing channels have normal distribution when the three above mentioned groups are being separated. The second step includes an exact specification for different types or classes within the three selected groups. The Bayesian classification procedure with a subsequent input of information from different sensing channels is used for this purpose. A software and hardware complex was developed to test the proposed method. It uses the information format from Resurs-P Russian spacecraft. The results of testing confirmed the functionality and high reliability of the proposed method. The reasons for hindering the implementation of the method were analyzed. These reasons are considered to be the absence of systematic studies on the reflection spectra of weeds, diseases and pests for individual crops. The developed software and hardware complex can be an effective tool in such studies.
Keywords: remote sensing, weeds, diseases, pests, detection, Bayesian procedures
Full text

References:

  1. Antonov V.N., Sladkikh L.A. Monitoring sostoyaniya posevov i prognozirovanie urozhainosti yarovoi pshenitsy po dannym DZZ (Monitoring of a condition of crops and forecasting of productivity of a spring-sown field according to DZZ), Geomatika, 2009, No. 4, pp. 50–53.
  2. Arkhipova O.E., Kachalina N.A., Tyutyunov Yu.V., Kovalev O.V. Otsenka zasorennosti antropogennykh fitotsenozov na osnove distantsionnogo zondirovaniya zemli (na primere ambrozii polynolistnoi) (Estimation of a contamination of anthropogenous fitotsenosis on the basis of remote sensing of the earth (on the example of an ambrosia wormwood-leaved)), Issledovanie Zemli iz kosmosa, 2014, No. 6, pp. 15–26.
  3. DeGroot M.H, Optimal'nye statisticheskie resheniya (Optimum statistical decisions), Moscow: Mir, 1974, 491 p.
  4. Kochubei S.M., Shadchina T.M., Kobets N.I. Spektral'nye svoistva rastenii kak osnova metodov distantsionnoi diagnostiki (Spectral properties of plants as basis of methods remote diagnostics), Kiev: Naukova dumka, 1990, 134 p.
  5. Milen'kii A.V. Klassifikatsiya signalov v usloviyakh neopredelennosti (Classification of signals in the conditions of uncertainty), Moscow: Sovetskoe radio, 1975, 309 p.
  6. Mikhailenko I.M. Osnovnye zadachi otsenivaniya sostoyaniya posevov i pochvennoi sredy po dannym kosmicheskogo zondirovaniya (The main objectives of estimation of a condition of crops and the soil environment according to space sounding), Ekologicheskie sistemy i pribory (Ecological systems and devices), 2011, No. 8, pp. 17–25.
  7. Mikhailenko I.M., Plotnikov A.M. Informativnost' distantsionnykh zondiruyushchikh izmeritelei sostoyaniya posevov i pochvennoi sredy (Informational content of the remote probing measuring instruments of a condition of crops and soil), Ekologicheskie sistemy i pribory (Ecological systems and devices), 2010, No. 4, pp. 32–36.
  8. Omel'chenko V.A. Osnovy spektral'noi teorii raspoznavaniya obrazov (Bases of the spectral theory of image identification), Khar'kov: Vishcha shkola, 1983, 159 p.
  9. Sultangazin U.M., Muratova N.R., Doraisvami R., Terekhov A.G. Otsenka sanitarnogo sostoyaniya sel’skokhozyaistvennykh ugodii s pomoshch’yu dannykh distantsionnogo zondirovaniya (Estimation of a sanitary condition of agricultural grounds by means of remote sensing), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2004, pp. 286-290.
  10. Shannon C. Raboty po teorii informatsii i kibernetike (Works on the theory of information and cybernetics), Moscow: Izd. inostrannoi literatury, 1963, 295 p.
  11. Crippen R.E. Calculating the Vegetation Index Faster, Remote Sensing of Environment, 1990, Vol. 34, pp. 71–73.
  12. Datt B. A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests Using Eucalyptus Leaves, Journal of Plant Physiology, January 1999, Vol. 1, pp. 30–36.
  13. Gamon J.A., Serrano L., Surfus J.S. The Photochemical Reflectance Index: An Optical Indicator of Photosynthetic Radiation Use Efficiency Across Species, Functional Types and Nutrient Levels, Oecologia, 1997, Vol. 112, Issue 4, pp. 492–501.
  14. Mikhailenko I.M. Assessment of crop and soil state using satellite remote sensing data, International Journal of Information Technology & Operations Management, August 2013, Vol. 1, No. 5, pp. 41–52.
  15. Penuelas J., Baret F., Filella I. Semi-Empirical Indices to Assess Carotenoids/Chlorophyll-a Ratio from Leaf Spectral Reflectance, Photosynthetica, 1995, Vol. 312, pp. 221–230.
  16. Sims D.A., Gamon J.A. Relationships Between Leaf Pigment Content and Spectral Reflectance Across a Wide Range of Species, Leaf Structures and Developmental Stages, Remote Sensing of Environment, 2002, pp. 337–354.