Drone-based Monitoring of Sunflower Crops
Abstract
Remote monitoring and utilization of digital technologies is essential for the application of the precision farming approach, which contributes significantly to the improved quality of agricultural products. The paper compares the data for six vegetation indices when observing the sunflower vegetation in South Dobrudzha in 2021. Images with RGB and digital NIR camera were obtained via a remotely piloted quadcopter. The flight plan specifies speed 8 m/s, altitude 100 m and shooting overlapping images of 80%. Six vegetation indices: NDVI, EVI2, SAVI, CVI, MGVRI and MPRI were calculated from the images obtained during the flight. The calculation of the indices takes into account the intensity of solar radiation and the parameters of the meteorological situation at the time of shooting. The findings obtained reveal a stable trend of change of the vegetation indices, thus, establishing accurate and reliable results as for the monitoring of agricultural areas with unmanned aerial vehicles.
References
Baret, F., Jacquemoud, S. & Hanocq, J. F. (1993). The Soil Line Concept in Remote Sensing. Remote Sensing Reviews, 7(1), 65-82. https://doi.org/10.1080/02757259309532166
Bannari, A., Morin, D., Huete, A.R. & Bonn, F. (1995). Our view of vegetation indices. Remote Sensing Reviews, 13(1-2), 95-120. https://doi.org/10.1080/02757259509532298
Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J. & Bareth, G. (2015). Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 39, 79-87. https://doi.org/10.1016/j.jag.2015.02.012
Burgess, D. W., Lewis, P., & Muller, J.-P.A.L. (1995). Topographic Effects in AVHRR NDVI Data. Remote Sensing of Environment, 54, 223-232. https://doi.org/10.1016/0034-4257(95)00155-7
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295-309. https://doi.org/10.1016/0034-4257(88)90106-X
Myneni, R.B. & Asrar, G. (1994). Atmospheric Effects and Spectral Vegetation Indices. Remote Sensing of Environment, 17, 390-402. https://doi.org/10.1016/0034-4257(94)90106-6
Mihaylov, R. (2019). Using drones to track development of maize cultivation. Farm machinery and Processes Management in sustainable Agriculture, Machinery Equipment, Lublin, Poland 2019, 43-48. https://doi.org/10.24326/fmpmsa.2019.1
Mihaylov, R., Atanasov, A., Ivanova, A., & Mihaylova, D. (2020). Study of the vegetation of spring crops in the region of South Dobrudhza in 2020. Annual Journal of Technical University of Varna (AJTUV), 4(2),122-129. https://doi.org/10.29114/ajtuv.vol4.iss2.203
Mihajlow, R., & Ivanova, A. (2019). Drone video capture - a new method in precision agriculture. Annual Journal of Technical University of Varna (AJTUV), 3(2) https://doi.org/10.29114/ajtuv.vol3.iss2.141
Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., & Harlan, J. C. (1974). Monitoring the vernal advancement of retrogradation of natural vegetation, p. 371. Greenbelt, MD: NASA/GSFC (Type III, Final Report).
Sankaran, S., Khot, L. R., Espinoza, C. Z., Jarolmasjed, S., Sathuvalli, V. R., Vandemark, G. J., et al. (2015). Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review. Eur. J. Agron., 70, 112-123. https://doi.org/10.1016/j.eja.2015.07.004
Shi, Y., Thomasson, J. A., Murray, S. C., Pugh, N. A., Rooney, W. L., Shafian, S., et al. (2016). Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PLoS One, 11(7) Article e0159781. https://doi.org/10.1371/journal.pone.0159781
Teillet, P.M., Staenz, K. & Williams, D.J. (1997). Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions. Remote Sensing of Environment, 61, 139-149. https://doi.org/10.1016/S0034-4257(96)00248-9
Vincini M., Frazzi, E. & D' Alessio, P. (2008). A broad-band leaf chlorophyll vegetation index at the canopy scale. Precision Agric, 9, 303–319. https://doi.org/10.1007/s11119-008-9075-z
Yang, Z., Willis, P. & Mueller, R. (2008). Impact of band-ratio enhanced AWIFS image to crop classification accuracy. Pecora 17, 18-20. Department of Health and Ageing. (2012). Aboriginal and Torres Strait Islander health performance framework 2012 report.
Yang, G., Liu, J., Zhao, C., Li, Z., Huang, Y., Yu, H., et al. (2017). Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and Perspectives. Front. Plant Sci., 8, https://doi.org/10.3389/fpls.2017.01111
Watson, D.J. (1947). Comparative physiological studies on the growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties and within and between years. Annals of Botany., 11, 41-76. https://doi.org/10.1093/oxfordjournals.aob.a083148
Zhangyan J., Alfredo R.H., Kamel D., & Tomoaki M. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112 (10), 3833-3845. https://doi.org/10.1016/j.rse.2008.06.006
Total number of hits on abstract = 422 times
Downloads for 2024
This work is licensed under a Creative Commons Attribution 4.0 International License.
