Methodical approaches for the identification of plants in the optical range by monitoring crops using Unmanned aerial vehicles (UAVs)
DOI:
https://doi.org/10.31073/acss89-10Keywords:
object image; remote monitoring; Unmanned aerial vehicles; UAVs; winter wheatAbstract
The use of Unmanned aerial vehicles (UAVs) as a platform for sensor equipment, should extend the potential of spectral research and enable the acquisition of data suitable for crop management. The purpose of the work was to develop a method of programmatic identification on digital images of pixels crops of, which correspond exactly to the plants, as well as to evaluate the horizontal projection of the dome of plants. The identification of plantations for crops of continuous sowing was performed in 2017-2018 at an experimental site to study the system of application of fertilizers National University of Life and Environmental Sciences of Ukraine on the example of wheat winter variety Colonia in the stage of vegetation tillering and the exit to a tube. Shooting was carried out with the help of UAV Phantom 3+ from a height of 40-100 meters with FC200 camera. Digital processing of jpeg format data was performed in MathCAD, where the graphic data was viewed as a matrix whose number of columns was three times that of the pixels horizontally of the original image. The effectiveness of cascade filters and specialized EGVI, ERVI, and MNVI indices was investigated. It was found that cascade soil filtration under the conditions of optical monitoring of plantations proved not to be effective enough for continuous seeding crops. EGVI, ERVI and MNVI optical indices for plant identification, designed for heights of 5-7 meters, have been found to be incapable of altitudes of 40 m and above, so their use for industrial production in these conditions is impractical. Based on the obtained results, they developed a technique for identifying plants for industrial use based on spectral portraits of plants based on estimating the difference between the intensities of the green and blue pixel channels. It has been proposed to use vegetation indices for the identification of continuous seeding crops to use the horizontal projection of the dome of plants as a fraction of the total area of the image belonging to the dome of plants
References
References
Pearse G.D., Watt M.S., Morgenroth J. 2016. Comparison of optical LAI measurements under diffuse and clear skies after correcting for scattered radiation. Agricultural and Forest Meteorology. Vol. 221. P. 61-70. DOI: https://doi.org/10.1016/j.agrformet.2016.02.001.
Guangjian Y., Ronghai H., Jinghui L., Marie W., Hailan J., Xihan M., Donghui X., Wuming Z. 2019. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agricultural and Forest Meteorology. Vol. 265. P. 390–411. DOI: https://doi.org/10.1016/j.agrformet.2018.11.033.
Pan Y., Han y., Wang L., Chen J., Meng H., Wang G., Zhang Z., Wang S. 2019. Reconstruction of Ground Crops Based on Airborne LiDAR Technology. IFAC-PapersOnLine. Vol.52 (24). P. 35-40. DOI: https://doi.org/10.1016/j.ifacol.2019.12.376.
Guoa T., Fanga Y., Chenga T., Tiana Y., Zhua Y., Chenb Q., Qiua X., Yaoa X. 2019. Detection of wheat height using optimized multi-scan mode of LiDAR during the entire growth stages. Computers and Electronics in Agriculture. Vol.165. Article 104959. DOI: https://doi.org/10.1016/j.compag.2019.104959.
Dorigo W.A., Zurita-Milla R., de Wit A.J.W., Brazile J., Singh R., Schaepman M.E. 2007. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation. Vol.9. P. 165–193. DOI: https://doi:10.1016/j.jag.2006.05.003.
Thomson B.D., Siddique K.H.M. 1997. Grain legume species in low rainfall Mediterranean-type environments II. Canopy development, radiation interception, and dry-matter production. Field Crops Research. Vol. 54, (2–3). P. 189-199. DOI: https://doi.org/10.1016/S0378-4290(97)00048-8.
Baret F., de Solana B., Lopez-Lozano R., Ma K., Weiss M. 2010. GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5º zenith angle: Theoretical considerations based on 3D architecture models and application to wheat crops. Agricultural and Forest Meteorology. Vol.150. P. 1393–1401. DOI: https://doi:10.1016/j.agrformet.2010.04.011.
Verger A., Vigneau N., Chéron C., Gilliot J.M., Comar A., Baret F. 2014. Green area index from an unmanned aerial system over wheat and rapeseed crops. Remote Sensing of Environment. Vol.152. P. 654-664. DOI: https://doi.org/10.1016/j.rse.2014.06.006.
Castillejo-González I.L., Pena-Barragán J.N., Jurado-Expósito M., Mesas-Carrascosa F.J., López-Granados F. 2014. Evaluation of pixel- and object-based approaches for mapping wildoat (Avena sterilis) weed patches in wheat fields using QuickBirdimagery for site-specific management. European Journal of Agronomy. Vol.59. P. 57-66 DOI: https://doi.org/10.1016/j.eja.2014.05.009.
Torres-Sánchez J., López-Granados F., Peña J.M. 2015. An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture. Vol.114. P. 43–52. DOI: https://doi.org/10.1016/j.compag.2015.03.019.
Jin X., Liu S., Baret F., Hemerlé M., Comar A. 2017. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment. Vol.198. P. 105-114. DOI: http://dx.doi.org/10.1016/j.rse.2017.06.007.
Dolia M., Lysenko V., Pasichnyk N., Opryshko O., Komarchuk D., Miroshnyk V., Lendiel T., Martsyfei A. 2019. Information Technology for Remote Evaluation of after Effects of Residues of Herbicides on Winter Crop Rape. 3rd International Conference on Advanced Information and Communications Technologies, AICT 2019 – Proceedings. P. 469-473. DOI: https://doi.org/10.1109/AIACT.2019.8847850.
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. International Scientific-Practical Conference on Problems of Info communications Science and Technology, PIC S and T 2018 – Proceed. P. 35-38. DOI: https://doi.org/10.1109/INFOCOMMST.2018.8632090.
De Rainville F.M., Durand A., Fortin F.-A., Tanguy K., Maldague K., Panneton X., Simard B., Pattern M.-J. 2012. Analysis and Applications, Bayesian classification and unsupervised learning for isolating weeds in row crops. Pattern Analysis and Applications. Vol.17(2). P. 401–414. DOI: https://doi.org/10.1007/s10044-012-0307-5.
Enciso J., Avila C.A., Jung J., Elsayed-Farag S., Chang A., Yeom J., Landivar J., Maeda M., Chavez J.C. 2019. Validation of agronomic UAV and field measurements for tomato varieties. Computers and Electronics in Agriculture. Vol.158. P. 278–28. DOI: https://doi.org/10.1016/j.compag.2019.02.011.
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