A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm history data

J. Kaivosoja, L. Pesonen, J. Kleemola, I. Pölönen, H. Salo, E. Honkavaara, Heikki Saari, Jussi Mäkynen, A. Rajala

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

14 Citations (Scopus)

Abstract

Different remote sensing methods for detecting variations in agricultural fields have been studied in last two decades. There are already existing systems for planning and applying e.g. nitrogen fertilizers to the cereal crop fields. However, there are disadvantages such as high costs, adaptability, reliability, resolution aspects and final products dissemination. With an unmanned aerial vehicle (UAV) based airborne methods, data collection can be performed cost-efficiently with desired spatial and temporal resolutions, below clouds and under diverse weather conditions. A new Fabry-Perot interferometer based hyperspectral imaging technology implemented in an UAV has been introduced. In this research, we studied the possibilities of exploiting classified raster maps from hyperspectral data to produce a work task for a precision fertilizer application. The UAV flight campaign was performed in a wheat test field in Finland in the summer of 2012. Based on the campaign, we have classified raster maps estimating the biomass and nitrogen contents at approximately stage 34 in the Zadoks scale. We combined the classified maps with farm history data such as previous yield maps. Then we generalized the combined results and transformed it to a vectorized zonal task map suitable for farm machinery. We present the selected weights for each dataset in the processing chain and the resultant variable rate application (VRA) task. The additional fertilization according to the generated task was shown to be beneficial for the amount of yield. However, our study is indicating that there are still many uncertainties within the process chain.
Original languageEnglish
Title of host publicationRemote Sensing for Agriculture, Ecosystems, and Hydrology XV
PublisherInternational Society for Optics and Photonics SPIE
ISBN (Print)978-081949756-7
DOIs
Publication statusPublished - 2013
MoE publication typeNot Eligible
EventSPIE Remote Sensing, 2013, Dresden, Germany - Dresden, Germany
Duration: 23 Sep 201326 Sep 2013

Publication series

NameProceedings of SPIE
PublisherSPIE
Volume8887
ISSN (Print)0277-786X

Conference

ConferenceSPIE Remote Sensing, 2013, Dresden, Germany
CountryGermany
CityDresden
Period23/09/1326/09/13

Fingerprint

fertilizer application
wheat
farm
history
raster
nitrogen
interferometer
cost
machinery
cereal
flight
vehicle
fertilizer
remote sensing
crop
biomass
summer
method

Keywords

  • farm machinery
  • fertilizer
  • hyperspectral
  • precision farming
  • task
  • UAV
  • VRA
  • wheat

Cite this

Kaivosoja, J., Pesonen, L., Kleemola, J., Pölönen, I., Salo, H., Honkavaara, E., ... Rajala, A. (2013). A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm history data. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XV [88870H] International Society for Optics and Photonics SPIE. Proceedings of SPIE, Vol.. 8887 https://doi.org/10.1117/12.2029165
Kaivosoja, J. ; Pesonen, L. ; Kleemola, J. ; Pölönen, I. ; Salo, H. ; Honkavaara, E. ; Saari, Heikki ; Mäkynen, Jussi ; Rajala, A. / A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm history data. Remote Sensing for Agriculture, Ecosystems, and Hydrology XV. International Society for Optics and Photonics SPIE, 2013. (Proceedings of SPIE, Vol. 8887).
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abstract = "Different remote sensing methods for detecting variations in agricultural fields have been studied in last two decades. There are already existing systems for planning and applying e.g. nitrogen fertilizers to the cereal crop fields. However, there are disadvantages such as high costs, adaptability, reliability, resolution aspects and final products dissemination. With an unmanned aerial vehicle (UAV) based airborne methods, data collection can be performed cost-efficiently with desired spatial and temporal resolutions, below clouds and under diverse weather conditions. A new Fabry-Perot interferometer based hyperspectral imaging technology implemented in an UAV has been introduced. In this research, we studied the possibilities of exploiting classified raster maps from hyperspectral data to produce a work task for a precision fertilizer application. The UAV flight campaign was performed in a wheat test field in Finland in the summer of 2012. Based on the campaign, we have classified raster maps estimating the biomass and nitrogen contents at approximately stage 34 in the Zadoks scale. We combined the classified maps with farm history data such as previous yield maps. Then we generalized the combined results and transformed it to a vectorized zonal task map suitable for farm machinery. We present the selected weights for each dataset in the processing chain and the resultant variable rate application (VRA) task. The additional fertilization according to the generated task was shown to be beneficial for the amount of yield. However, our study is indicating that there are still many uncertainties within the process chain.",
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Kaivosoja, J, Pesonen, L, Kleemola, J, Pölönen, I, Salo, H, Honkavaara, E, Saari, H, Mäkynen, J & Rajala, A 2013, A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm history data. in Remote Sensing for Agriculture, Ecosystems, and Hydrology XV., 88870H, International Society for Optics and Photonics SPIE, Proceedings of SPIE, vol. 8887, SPIE Remote Sensing, 2013, Dresden, Germany, Dresden, Germany, 23/09/13. https://doi.org/10.1117/12.2029165

A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm history data. / Kaivosoja, J.; Pesonen, L.; Kleemola, J.; Pölönen, I.; Salo, H.; Honkavaara, E.; Saari, Heikki; Mäkynen, Jussi; Rajala, A.

Remote Sensing for Agriculture, Ecosystems, and Hydrology XV. International Society for Optics and Photonics SPIE, 2013. 88870H (Proceedings of SPIE, Vol. 8887).

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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AU - Kaivosoja, J.

AU - Pesonen, L.

AU - Kleemola, J.

AU - Pölönen, I.

AU - Salo, H.

AU - Honkavaara, E.

AU - Saari, Heikki

AU - Mäkynen, Jussi

AU - Rajala, A.

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AB - Different remote sensing methods for detecting variations in agricultural fields have been studied in last two decades. There are already existing systems for planning and applying e.g. nitrogen fertilizers to the cereal crop fields. However, there are disadvantages such as high costs, adaptability, reliability, resolution aspects and final products dissemination. With an unmanned aerial vehicle (UAV) based airborne methods, data collection can be performed cost-efficiently with desired spatial and temporal resolutions, below clouds and under diverse weather conditions. A new Fabry-Perot interferometer based hyperspectral imaging technology implemented in an UAV has been introduced. In this research, we studied the possibilities of exploiting classified raster maps from hyperspectral data to produce a work task for a precision fertilizer application. The UAV flight campaign was performed in a wheat test field in Finland in the summer of 2012. Based on the campaign, we have classified raster maps estimating the biomass and nitrogen contents at approximately stage 34 in the Zadoks scale. We combined the classified maps with farm history data such as previous yield maps. Then we generalized the combined results and transformed it to a vectorized zonal task map suitable for farm machinery. We present the selected weights for each dataset in the processing chain and the resultant variable rate application (VRA) task. The additional fertilization according to the generated task was shown to be beneficial for the amount of yield. However, our study is indicating that there are still many uncertainties within the process chain.

KW - farm machinery

KW - fertilizer

KW - hyperspectral

KW - precision farming

KW - task

KW - UAV

KW - VRA

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U2 - 10.1117/12.2029165

DO - 10.1117/12.2029165

M3 - Conference article in proceedings

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PB - International Society for Optics and Photonics SPIE

ER -

Kaivosoja J, Pesonen L, Kleemola J, Pölönen I, Salo H, Honkavaara E et al. A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm history data. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XV. International Society for Optics and Photonics SPIE. 2013. 88870H. (Proceedings of SPIE, Vol. 8887). https://doi.org/10.1117/12.2029165