Determination of the height of the wheat plant with the data obtained from different unmanned aerial vehicles (UAVs)

Year 2021, , 195 - 203, 02.08.2021

Namık Kemal Sönmez , Mesut Çoşlu , Nusret Demir

https://doi.org/10.29136/mediterranean.823440

Cited By: 2

Abstract

In this study, it is aimed to calculate the plant height of durum wheat variety on three different dates using unmanned aerial vehicles (UAV) with different characteristics semi-automatically. The study was carried out in April, which is the period when wheat passes from vegetative to generative period and which is also considered the most suitable date in remote sensing studies for the Mediterranean region. Unmanned aerial vehicle data were obtained in three different date intervals in April, and ground measurements were made simultaneously with the UAV shots. The data obtained from the unmanned aerial vehicles were taken from a height of 10 m and with appropriate overlap ratios. All aerial photographs were processed using the same procedures for the production of orthomosaic images, digital surface models (DSM) and digital terrain models (DTM). In the study, while determining the parcel boundaries of the trials on very high-resolution orthomosaic images, plant heights were calculated with the normalized digital surface model (nDSM) obtained by using DSM and DTM data. At the end of the study, the plant heights calculated semi-automatically were compared with the plant heights measured in the field in the same area. As a result of the statistical analysis between the calculated plant height values and field measurement values, the highest correlation was found (r= 0.948) for Phantom 3 Advanced UAV on April 16, 2020; (r= 0.886) for Mavic Pro UAV on April 10, 2020; (r= 0.924) for Inspire 2 UAV on April 22, 2020. According to the result of the research, it has been revealed that the plant height can be determined safely with unmanned aerial vehicles with different characteristics.

Keywords

Unmanned aerial vehicle, Wheat, Plant height, Normalized digital surface model

Farklı insansız hava araçlarından (İHA) elde edilen veriler ile buğday bitkisinin boyunun belirlenmesi

Abstract

Bu çalışmada farklı özelliklere sahip insansız hava araçları kullanılarak (İHA), üç ayrı tarihte çekimi yapılan makarnalık buğday çeşidinin bitki boylarının yarı otomatik olarak hesaplanması amaçlanmıştır. Çalışma, Akdeniz bölgesi için uzaktan algılama çalışmalarında en uygun tarih olarak kabul edilen ve buğdayın vejetatif dönemden generatif döneme geçtiği nisan ayında gerçekleştirilmiştir. İnsansız hava aracı verileri nisan ayı içerisindeki üç farklı tarih aralığında temin edilmiş olup, İHA çekimleri ile eş zamanlı olarak arazide yersel ölçümler de yapılmıştır. İnsansız hava araçlarından alınan veriler 10 m yükseklikten ve uygun bindirme oranları ile elde edilmiştir. Tüm hava fotoğrafları ortomozaik görüntü, sayısal yüzey modeli (DSM) ve sayısal arazi modeli (DTM) üretimi amacıyla aynı prosedürler uygulanarak işlenmiştir. Çalışmada çok yüksek çözünürlüklü ortomozaik görüntüler üzerinden denemelere ait parsel sınırları belirlenirken, DSM ve DTM verileri kullanılarak elde edilen normalize edilmiş sayısal yüzey modeli (nDSM) ile bitki boyları hesaplanmıştır. Çalışma sonunda yarı otomatik olarak hesaplanan bitki boyları, aynı alandaki araziden ölçülen bitki boyları ile karşılaştırılmıştır. Hesaplanan bitki yükseklik değerleri ile arazi ölçüm değerleri arasında yapılan istatistiksel analizler sonucunda en yüksek ilişkiler, Phantom 3 Advanced İHA’sı için (r= 0.948) 16 Nisan 2020 tarihinde, Mavic Pro İHA’sı için (r= 0.886) 10 Nisan 2020 tarihinde ve Inspire 2 İHA’sı için ise (r= 0.924) 22 Nisan 2020 tarihinde elde edilmiştir. Araştırma sonucuna göre, bitki boyunun farklı özelliklere sahip insansız hava araçları ile güvenli bir şekilde belirlenebileceği ortaya konmuştur.

Keywords

İnsansız hava aracı, Buğday, Bitki boyu, Normalize edilmiş sayısal yüzey modeli

References

• Bendig J, Willkomm M, Tilly N, Gnyp ML, Bennertz S, Qiang C, Miao Y, Lenz-Wiedemann VIS, Bareth G (2013a) Very high resolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring in Northeast China. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 45-50.
• Bendig J, Bolten A, Bareth G (2013b) UAV-based imaging for multi-temporal, very high resolution crop surface models to monitor crop growth variability. Photogrammetrie Fernerkundung Geoinformation 551-562.
• Çoşlu M, Sönmez NK (2019) Normalize edilmiş sayısal yüzey modeli (nDSM) ile bitki boyu ölçümü ve verim ilişkisi. II. International Eurasian Agriculture and Natural Sciences Congress, Vol.1 No.1 Antalya, s. 271-278.
• Çubukçu KM (2015) Planlamada ve Coğrafyada Temel İstatistik ve Mekânsal İstatistik, Nobel Akademik Yayıncılık, Yayın No: 1097, Ankara.
• Demir N, Sönmez NK, Akar T, Ünal S (2018) Automated measurement of plant height of wheat genotypes using a DSM derived from UAV. Imagery Proceedings 2, pp. 350.
• DJI (2020a) Phantom 3 Advanced Specs. https://www.dji.com/phantom-3-adv/info. Accessed 10 September 2020.
• DJI (2020b) Mavic Pro Specs. https://www.dji.com/mavic/info. Accessed 10 September 2020.
• DJI (2020c) Inspire 2 Specs. https://www.dji.com/inspire-2/info. Accessed 10 September 2020.
• DJI (2020d) Zenmuse X4S spec. https://www.dji.com/zenmuse-x4s/info#specs. Accessed 10 September 2020.
• Dong X, Zhang Z, Yu R, Tian Q, Zhu X (2010) Extraction of information about ındividual trees from high-spatial-resolution UAV-acquired ımages of an orchard. Remote Sensing 12(1): 133.
• Epiphanio JCN, Formaggio AR, Franca GV (1990) Evaluation of Landsat-5 TM Bands in discriminating between wheat and bean crops. Pesquisa Agropecua'ria Brasilerira, 25(3): 371-377.
• ESRI (2021a) ArcMAP User manual, https: //desktop.arcgis.com/en/ arcmap /10.5/tools/data-management-toolbox/feature-to-point.htm. Accessed 27 January 2021.
• ESRI (2021b) ArcMAP User manual, https: //desktop.arcgis.com/en /arcmap/latest/tools/analysis-toolbox/buffer.htm. Accessed 27 January 2021.
• Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Pretty J, Robinson S, Thomas SM, Toulmin C (2010) Food security: The challenge of feeding 9 billion people. Science 327: 812-818.
• Han X, Thomasson JA, Bagnall GC, Pugh NA, Horne DW, Rooney WL, Jung J, Chang A, Malambo L, Popescu SC, Gates IT, Cope DA (2018) Measurement and calibration of plant-height from fixed-wing UAV Images. Sensors 18: 4092.
• Holman FH, Riche AB, Michalski A, Castle M, Wooster MJ, Hawkesford MJ (2016) High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sensing 8: 1031.
• Hu P, Chapman S, Wang X, Potgieter A, Duan T, Jordan D, Guo Y, Zheng B (2018) Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: Example for sorghum breeding. European Journal of Agronomy 95: 24-32.
• Panda SS, Hoogenboom G, Paz JO (2010) Remote sensing and geospatial technological applications for site-specific management of fruit and nut crops: A review. Remote Sensing 2: 1973-1997.
• Su W, Zhang M, Bian D, Liu Z, Huang J, Wang W, Wu J, Guo H (2019) Phenotyping of corn plants using unmanned aerial vehicle (UAV) images. Remote Sensing 11: 2021.
• Westoby MJ, Brasington J, Glasser NF, Hambrey MJ, Reynolds JM (2012) Structure-from-motion photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology 179: 300-314.
• Yao H, Qin R, Chen X (2019) Unmanned aerial vehicle for remote sensing applications-A review. Remote Sensing 11: 1443.
• Yuan W, Li J, Bhatta M, Shi Y, Baenziger PS, Ge Y (2018) Wheat height estimation using LIDAR in comparison to ultrasonic sensor and UAS. Sensors 18(11): 3731.
• Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Research 14: 415-421.