The method of determining of yield based on the results of remote sensing obtained using uav on the example of wheat
DOI:
https://doi.org/10.31548/energiya2019.05.063Abstract
Abstract. The methods of contact monitoring of conditions of plants are not adapted for large-scale industrial field studies. Satellite monitoring was primarily created to objectively evaluate the prospects for harvesting. Usage of UAVs has allowed to improve the accuracy of the results due to the high resolution of the sensor equipment. In most cases, the prediction is based on standard NDVI stress indexes. However, these results do not provide information on quantitative of yield indicators that farms need in order to optimize logistics and use harvesting equipment. The development of a methodology for interpreting the results of remote monitoring of vegetation/stress indices into future volumes of the crop was the goal of this work.
Researches were conducted in 2019 in the Kiev region on the production area of winter wheat crops in NULES "Agronomic Research Station". Wheat was harvested using a John Deere 9670STS combine. During the operation of the harvesting the data of John Deere 9670STS were recorded every second. In this case, more than 14,000 separate sections were obtained with a header width of 9 meters. Data were processed using Trimble (R) Farm Works (R) Office software and exported as Microsoft Excel. A specialized SlantRange 3p spectral system was used for remote monitoring, which was mounted on the DJI Matrice 600 Pro industrial platform. The radio frequency correction for changes in lighting was done by standard anti-aircraft sensor. The results were calculated and the stress index maps were constructed using the software of the SlantView system developer.
In usage obtained experimental data was developed the method of comparing the objective harvest data obtained by the harvester in terms of grain count and UAV stress index values. Was proposed to use a stress index, which makes it possible to predict the value of future harvest (the map of the stress indices was built 2 months before the harvest). It results can be used by farm to optimize the use of ground equipment.
Key words: SlantRange, stress index, UAV
References
Savin, I. Yu., Vernyuk, Yu .I., Faraslis, G. (2015) The possible use of pilotless aircrafts for operative monitoring of soil productivity. Bulletin of the V.V. Dokuchaev Soil Institute, 80, 95-105.
https://doi.org/10.19047/0136-1694-2015-80-95-105
Savin, I. Yu., Kozubenko, I. S. (2018) Possibilities of satellite data usage in agricultural insurances. RUDN Journal of Agronomy and animal industries, 13 (4), 336-343.
https://doi.org/10.22363/2312-797X-2018-13-4-336-343
Mathyam Prabhakar, Gopinath, K. A., Reddy, A. G .K., Thirupathi, M., Ch. Srinivasa Rao (2019). Mapping hailstorm damaged crop area using multispectral satellite data. The Egyptian Journal of Remote Sensing and Space Science, 22 (1), 73-79.
https://doi.org/10.1016/j.ejrs.2018.09.001
Lai, Y.R. , Pringle, M.J., Kopittke, P.M., Menzies, N.W., Orton, T.G., Dang, Y.P. (2018). An empirical model for prediction of wheat yield, using time-integrated Landsat NDVI. International Journal of Applied Earth Observation and Geoinformation, 72, 99-108.
https://doi.org/10.1016/j.jag.2018.07.013
Geng Bai, Yufeng Ge, Waseem Hussain, P. Stephen Baenziger, George Graef (2016). A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding. Computers and Electronics in Agriculture, 128, 181-192/
https://doi.org/10.1016/j.compag.2016.08.021
Kyratzis, A., Skarlatos, D., Fotopoulos, V., Vamvakousis, V., Katsiotis, A. (2015). Investigating Correlation among NDVI Index Derived by Unmanned Aerial Vehicle Photography and Grain Yield under Late Drought Stress Conditions. Procedia Environmental Sciences, 29, 225-226.
https://doi.org/10.1016/j.proenv.2015.07.284
Saberioona, M.M., Amina, M.S.M., Anuarb, A.R., Gholizadehc, A., Wayayokd, A., Khairunniza-Bejoda Smart, S. (2014) Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. International Journal of Applied Earth Observation and Geoinformation,.32, 35-45.
https://doi.org/10.1016/j.jag.2014.03.018
Lysenko, V., Opryshko, O., Komarchuk, D., Pasichnyk, N., Zaets, N., Dudnyk, A. (2017) Usage of Flying Robots for Monitoring Nitrogen in Wheat Crops. The 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 1, 30-34.
https://doi.org/10.1109/IDAACS.2017.8095044
Korobiichuk, I., Lysenko, V., Opryshko, O., Komarchyk, D., Pasichnyk, N., Juś, A. (2018). Crop monitoring for nitrogen nutrition level by digital Automation 2018. AUTOMATION 2018. Advances in Intelligent Systems and Computing, 743. Springer, Cham, 595-603.
https://doi.org/10.1007/978-3-319-77179-3_56
Aitazaz A. Farooque, Young K. Chang, Qamar U. Zaman, Dominic Groulx, Arnold W. Schumann, Travis J. Esau (2013). Performance evaluation of multiple ground based sensors mounted on a commercial wild blueberry harvester to sense plant height, fruit yield and topographic features in real-time Computers and Electronics in Agriculture, 91, 135-144/
https://doi.org/10.1016/j.compag.2012.12.006
F. Pallottino, F.Antonucci, C.Costa, C.Bisaglia, S.Figorilli, P.Menesatti (2019). Optoelectronic proximal sensing vehicle-mounted technologies in precision agriculture: A review Computers and Electronics in Agriculture, 162, 859-873.
https://doi.org/10.1016/j.compag.2019.05.034
Jesús Martín Talavera, Luis Eduardo Tobón, Jairo Alejandro Gómez, María Alejandra Culman, Juan Manuel Aranda, Diana Teresa Parra, Luis Alfredo Quiroz, Adolfo Hoyos, Luis Ernesto Garreta (2017). Review of IoT applications in agro-industrial and environmental fields. Computers and Electronics in Agriculture, 142 (A), 283-297.
https://doi.org/10.1016/j.compag.2017.09.015
Lysenko, V., Komarchuk, D., Pasichnyk, N., Opryshko, O., Awtoniuk, M., Martsyfei, A. (2018). Information Support of Remote Monitoring of Grain Crops Biomass Amount as the Feedstock to Load Biogas Reactors. 2018 International Scientific-Practical Conference on Problems of Infocommunications Science and Technology, 35-38.
https://doi.org/10.1109/INFOCOMMST.2018.8632090
Shvorov, S.A., Komarchuk, D. S., Pasichnyk, N. A., Opryshko, O. O., Gunchenko, Yu. A., Kuznichenko, S. D. (2018). UAV Navigation and Management System Based on the Spectral Portrait of Terrain. 2018 IEEE 5th International Conference on Methods and Systems of Navigation and Motion Control, MSNMC 2018, Proceedings, 68-71.
https://doi.org/10.1109/MSNMC.2018.8576304
Pasichnyk, N. A., Opryshko, O. O., Komarchuk, D. S., Miroshnyk, V. O. (2019). Experience in using mathcad to analyze data from UAVS for remote sensing of crops. Naukovyi visnyk NUBiP Ukrainy. Seriia: Ahronomiia, 244-250.
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