Special features of grain crops spectral analysis using UAV
Abstract
Abstract. The monitoring of the agriculture fields vegetation state is a significant step in using of remote sensing for precision agriculture. No traditional airborne platforms like aircrafts and commercial satellites fit these uses because of their low resolution images. The problem can be solved by using UAVs.
In this article, UAV being equipped with visible spectrum camera was used to produce an image of the wheat field in tillering phase. Due to the high image resolution on photo image soil is also fixed as well as the plants. It will affect on the total spectral indicators of the planted area.
The aim of the study is to develop a methodology of distinguishing the wheat area from the soil area on photo images. Experiments were carried out within 40 – 100 m UAV’s heights range for different states of soil – arable seedbed and dry dirt road. It has been established that it is important for f arable land being mostly fixed for sowings and, accordingly, the adjustment of filters should be carried out for each height monitoring apartly.
While selecting optical range filtration canal to distinguish plants from the soil while being analyzed by separated pixels of image it would be appropriate to use green and blue canals.
Key words: UAV, stress indices, nitrogen, harvesting routes, NDVI, remote monitoring, agricultural crops, spectral index.
References
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Mónica Herrero-Huerta, David Hernández-López, Pablo Rodriguez-Gonzalvez, Diego González-Aguilera, José González-Piqueras. Vicarious radiometric calibration of a multispectral sensor from an aerial trike applied to precision agriculture. Computers and Electronics in Agriculture, 2014. Vol. 108. P. 28–38.
Jianfeng Zhou, Lav R. Khot, Haitham Y. Bahlol, Rick Boydston, Phillip N. Miklas. Evaluation of ground, proximal and aerial remote sensing technologies for crop stress monitoring IFAC-PapersOnLine. 2016. Vol. 49, № 16, P. 22–26.
Vitalii Lysenko, Oleksiy Opryshko, Dmytro Komarchuk, Nadiia Pasichnyk, Nataliia Zaets, Alla Dudnyk. 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 (21-23.09.2017), 2017. P. 30–34.
Jesper Rasmussena, Georgios Ntakos, Jon Nielsen, Jesper Svensgaard, Robert N. Poulsen, Svend Christensen. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? European Journal of Agronomy. Vol. 74, 2016. P. 75–92 http://dx.doi.org/10.1016/j.eja.2015.11.026.
V. Lysenko, O. Opryshko, D. Komarchyk, N. Pasichnik. Drones camera calibration for the leaf research. Scientific herald of the National University of Bioresources and Nature Management of Ukraine. Series: Engineering and Power Engineering of Agroindustrial Complex. Vol. 252. 2016. P. 61–65. http://nbuv.gov.ua/UJRN/nvnau_tech_2016_25_10
Gunchenko, Y. A., Shvorov, S. A., Rudnichenko, N. D., Boyko, V. D. Methodical complex of accelerated training for operators of unmanned aerial vehicles. 2016 IEEE 4th International Conference Methods and Systems of Navigation and Motion Control, MSNMC 2016 –Proceedings. 2016. https://www.scopus.com/authid/detail. uri? authorId=57193057973.
Jan U.H. Eitel, Troy S. Magneya, Lee A. Vierlinga, Tabitha T. Brownc, David R. Huggins. LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status. Field Crops Research. 2014. 159. P. 21–32.
Shouyang Liu, Fred Baret Mariem Abichou, Fred Boudon, Samuel Thomas, Kaiguang Zhao, Christian Fournier, Bruno Andrieu, Kamran Irfan, Matthieu Hemmerlé, Benoit de Solan. Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model Agricultural and Forest Meteorology. 2017. Vol. 247. P. 12–20.
Xi Zhu, Tiejun Wang, Roshanak Darvishzadeh, Andrew K. Skidmore, K. Olaf Niemann. 3D leaf water content mapping using terrestrial laser scanner backscatter intensity with radiometric correction. Journal of Photogrammetry and Remote Sensing. 2015. 110, P. 14–23.
Hoffmeister, D. Chapter 11: Laser Scanning Approaches for Crop Monitoring. Comprehensive Analytical Chemistry, 2016. Volume 74, P. 343–361.
Richardson, A. J.; Wiegand, C. L. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing. 1977. Vol. 43. № 2. P. 1541–1552.
Carlos de Souza, Rubens Lamparelli, Jansle Rocha, Paulo Magalhães. Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images. Computers and Electronics in Agriculture. 2017. Vol. 143. P. 49–56.
J. Torres-Sánchez, J.M. Peña, A.I. de Castro, F. López-Granados. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV Computers and Electronics in Agriculture. 2014. Vol. 103. P. 104–113.
María Pérez-Ortiz, José Manuel Peña, Pedro Antonio Gutiérrez, JorgeTorres-Sánchez, César Hervás-Martínez, Francisca López-Granados. Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery Expert Systems with Applications. 2016. Vol. 47. P. 85–94.
Ghamisi, P., Couceiro, M. S., Benediktsson, J. A., Ferreira, N. M. An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Systems with Applications, 2012. 39, P. 12407–12417. URL: http://www.sciencedirect.com/ science/article/pii/ S0957417412006756. doi:http://dx.doi. org/10.1016/j.eswa.2012.04.078.
J. Senthilnath, Manasa Kandukuri, Akanksha Dokania, K.N. Ramesh. Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods. Computers and Electronics in Agriculture. 2017. Vol. 140. P. 8–24.
Blaschke, T. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 2010. Vol. 65. P. 2–16.
P´erez-Ortiz, M., Guti´errez, P. A., Pe˜na, J. M., Torres-S´anchez, J., Herv´as-Mart´ınez, C., & L´opez-Granados, F. An experimental comparison for the identification of weeds in sunflower crops via unmanned aerial vehicles and object-based analysis. Advances in Computational Intelligence. Springer International Publishing. 2015. Vol. 9094 of Lecture Notes in Computer Science. P. 252–262.
Junfeng Gao, Wenzhi Liao, David Nuyttens, Peter Lootens, Jürgen Vangeyte, Aleksandra Pižurica, Yong He, Jan G. Pieters. Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery. International Journal of Applied Earth Observation and Geoinformation. 2018. Vol. 67, P. 43–53.
I. Korobiichuk, V. Lysenko, O. Opryshko, D. Komarchyk, N. Pasichnyk, A. Juś. Crop Monitoring for Nitrogen Nutrition Level by Digital Camera, Automation 2018, AISC, 2018. volume 743. P. 595–603. (https://link.springer.com/chapter/10.1007/978-3-319-77179-3_56).
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