Winter wheat, winter rape and poppy crop growth evaluation with the help of remote and proximal sensing measurements

Z. Jelínek¹*, K. Starý¹, J. Kumhálová¹, J. Lukáš² and J. Mašek³

¹Czech University of Life Sciences Prague, Faculty of Engineering, Department of Machinery Utilization, Kamýcká 129, CZ165 00 Prague, Czech Republic
²Crop Research Institute, v.v.i., Drnovská 507, CZ161 00 Prague, Czech Republic
³Czech University of Life Sciences, Faculty of Engineering, Department of Agricultural Machines, Kamýcká 129, CZ165 00 Prague, Czech Republic
*Correspondence: jelinekzdenek@tf.czu.cz

Abstract:

Monitoring of agricultural crops with the help of remote and proximal sensors during the growing season plays important role for site-specific management decisions. Winter wheat, winter rape and poppy are representatives of typical agricultural crops from the family Poacea, Brassicaceae and Papaveraceae, growing in relative dry area of Rakovník district in the Czech Republic. Ten Sentinel 2 satellite images acquired during vegetation season of the crops were downloaded and processed. Crops were monitored with the help of unmanned aerial vehicles (UAV) equipped with consumer grade Red Green Blue (RGB) camera and multispectral (MS) MicaSense RedEdge MX camera. In-field variability was assessed by computing RGB-based vegetation indices Triangular Greenness Index (TGI), Green Leaf Index (GLI) and Visible Atmospherically Resistant Index (VARI) and commonly used vegetation indices as Normalised Difference Vegetation Index (NDVI) and Green NDVI (GNDVI). The results derived from satellite and UAV images were supported with in-situ measurements of hand-held GreenSeeker and Chlorophyll Meter Content sensors. The study showed the usability of individual vegetation indices, especially the TGI index for chlorophyll content estimation, and VARI index for green vegetation fraction detection and leaf area index estimation, in comparison with selected hand-held devices. The results showed as well that leaf properties and canopy structure of typical characteristics of selected families can significantly influence the spectral response of the crops detected in different phenological stages.

Key words:

poppy, satellite images, unmanned aerial vehicles, vegetation indices, winter rape, winter wheat

Comparing RGB – based vegetation indices from UAV imageries to estimate hops canopy area

K. Starý¹*, Z. Jelínek¹, J. Kumhálová¹, J. Chyba² and K. Balážová²

¹Czech University of Life Sciences Prague, Faculty of Engineering, Department of Machinery Utilization, Kamýcká 129, CZ165 00 Prague, Czech Republic
²Czech University of Life Sciences, Faculty of Engineering, Department of Agricultural Machines, Kamýcká 129, CZ165 00 Prague, Czech Republic
*Correspondence: staryk@tf.czu.cz

Abstract:

Remote estimation of hops plants in hop gardens is imperative in field of precision agriculture, because of precise imaging of hop garden structure. Monitoring of hop plant volume and area can help to predict the condition and yield of hops. In this study, two unmanned aerial vehicles (UAV) – eBee X senseFly UAV equipped with Red Green Blue (RGB) S.O.D.A. camera and Vertical Take-Off Landing (VTOL) UAV FireFly6 Pro by BirdsEyeView Aerobotics equipped with MicaSense RedEdge MX camera were used to acquire images of hop garden at phenology stage maturity of cones (24 th July) before harvest. Seven commonly used RGB vegetation indices (VI) were derived from these RGB and multispectral (MS) images after photogrammetric pre-processing and orthophoto mosaic extraction using Pix4Dmapper software. Vegetation Indices as the Green Percentage Index (G%), Excess of Green Index (ExGreen), Green Leaf Index (GLI), Visible Atmospherically Resistant Index (VARI), Red Green Blue Vegetation Index (RGBVI), Normalised Green Red Difference Index (NGRDI) and Triangular Greenness Index (TGI) were derived from both data sets. Binary model from each of VI was derived and threshold value for green vegetation was set. The results showed significant differences in hop plant area based on the specifications of cameras, especially wavelengths centres, and design and flight parameters of both UAV types. The comparison of various indices showed, that ExG and TGI indices has the highest congruity of estimated vegetation indices in hop garden canopy area for both used cameras. Further processing by Fuzzy Overlay tool proved high accuracy in green canopy area estimation for ExG and TGI vegetation indices.

Key words: