Hyperspectral imaging based precise fertilization is challenge in the northern Europe, because of the cloud conditions. In this paper we will introduce schemes for the biomass and nitrogen content estimations from hyperspectral images. In this research we used the Fabry-Perot interferometer based hypespectral imager that enables hyperspectral imaging from lightweight UAVs. During the summers 2011 and 2012 imaging and flight campaigns were carried out on the Finnish test field. Estimation mehtod uses features from linear and non-linear unmixing and vegetation indices. The results showed that the concept of small hyperspectral imager, UAV and data analysis is ready to operational use.
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.
A novel way to produce biomass estimation will offer possibilities for precision farming. Fertilizer prediction maps
can be made based on accurate biomass estimation generated by a novel biomass estimator. By using this knowledge,
a variable rate amount of fertilizers can be applied during the growing season. The innovation consists of light UAS, a
high spatial resolution camera, and VTT's novel spectral camera. A few properly selected spectral wavelengths with
NIR images and point clouds extracted by automatic image matching have been used in the estimation. The spectral
wavelengths were chosen from green, red, and NIR channels.