Effect of UAV Flight Altitude on Wheat Field Classification Accuracy and Operational Efficiency

Fadiş Rabia Yıldırım; Fatma Yıldırım Dalkıran; Mehmet Konar

doi:10.30518/jav.2022560

Effect of UAV Flight Altitude on Wheat Field Classification Accuracy and Operational Efficiency

Abstract

Flight altitude ties the quality of unmanned aerial vehicle (UAV) imagery to the cost of acquiring it, yet operational guidance on selecting it for crop mapping remains largely qualitative. This study quantifies the trade-off using a repeat-altitude survey of one winter wheat field in Kayseri, Türkiye. A 20 MP RGB camera on a fixed-nadir gimbal acquired 300 frames over the same ground at six altitudes between 10 m and 120 m (0.227–2.725 cm/pixel). After removing 25 frames whose gimbal yaw deviated from the flight heading, imagery was resampled onto a common 5 m × 5 m ground cell grid so that every altitude represents identical ground at an identical input size, making ground sampling distance (GSD) the only variable. A total of 183 colour, colour index and texture features were extracted from 11 251 cell tiles and classified with Random Forest under five-fold spatial block cross-validation with a 10 m buffer; the cell (n = 654) rather than the tile was the statistical unit. For three-class land cover over a 2× GSD range (60–120 m, n = 654), overall accuracy declined monotonically from 0.804 to 0.734 (McNemar p < 0.0001 for 60 m versus both coarser altitudes). For binary wheat extent mapping over a 6× GSD range (20–120 m, n = 186), accuracy was statistically flat from 20 m to 90 m (0.903, 0.909, 0.914; McNemar p = 0.75–1.00) and fell only at 120 m (0.866; p = 0.039 and 0.049); because the 20 m mission itself carried motion blur, this plateau compares effective resolution and not nominal GSD. Under a fixed 75 %/65 % overlap plan, 90 m required 17× fewer images, 19× less storage and 4–17× less flight time than 20 m for equal accuracy, identifying 90 m as the operational optimum for this task and sensor. Processing time did not decrease with altitude, because it scales with ground area and not with image count; a pipeline that classifies each ground cell once instead of once per overlapping frame ran 6–12× faster. A 40 m mission flown two hours later in weaker light required a 1/41 s exposure, which introduced approximately 12 pixels of motion blur and reduced effective sharpness to 34 % of the 60 m mission despite a finer nominal GSD, showing that nominal GSD is not effective resolution.

Keywords

Supporting Institution

Erciyes University

Project Number

FDK-2023-13046

Thanks

This work was supported by the Research Fund of Erciyes University Scientific Research Projects (BAP) Coordination Unit under Project Number FDK-2023-13046.

References

Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M. L., & 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.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Çelebi, Y., & Aydın, H. (2025). Multirotor unmanned aerial vehicle systems: An in-depth analysis of hardware, software, and communication systems. Journal of Aviation, 9(1), 225–240.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.
Demiray, E., Konar, M., & Arık Hatipoğlu, S. (2026). Development of a thermal helipad for UAVs and detection with deep learning. Drones, 10(4), 266.
Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1), 76–87.
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610–621.
Hunt, E. R., Cavigelli, M., Daughtry, C. S. T., McMurtrey, J. E., & Walthall, C. L. (2005). Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture, 6(4), 359–378.

Kahya, A., & Konar, M. (2026). Integration of artificial intelligence into unmanned aerial vehicles. Journal of Aviation, 10(2), 235–243.
Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021). Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 24–49.
Konar, M. (2020). Simultaneous determination of maximum acceleration and endurance of morphing UAV with ABC algorithm-based model. Aircraft Engineering and Aerospace Technology, 92(4), 579–586.
Konar, M., Arık Hatipoğlu, S., Erol, İ., Çam, Ö., Tuna, S., & Fenerci, M. (2026). A comparative study of machine learning and deep learning models for state of charge and remaining useful life estimation on a rotary-wing UAV battery. Drones, 10(7), 549.
Li, Y., Guo, S., Jia, S., Yan, Y., Jia, H., & Zhang, W. (2025). Quantifying the effects of UAV flight altitude on the multispectral monitoring accuracy of soil moisture and maize phenotypic parameters. Agronomy, 15(9), 2137.
Louhaichi, M., Borman, M. M., & Johnson, D. E. (2001). Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 16(1), 65–70.
Maes, W. H. (2025). Practical guidelines for performing UAV mapping flights with snapshot sensors. Remote Sensing, 17(4), 606.
Meyer, G. E., & Neto, J. C. (2008). Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture, 63(2), 282–293.
Ojala, T., Pietikäinen, M., & Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.
Oktay, T., Konar, M., Onay, M., Aydin, M., & Mohamed, M. A. (2016). Simultaneous small UAV and autopilot system design. Aircraft Engineering and Aerospace Technology, 88(6), 818–834.
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder, B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., & Dormann, C. F. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40(8), 913–929.
Sieberth, T., Wackrow, R., & Chandler, J. H. (2015). UAV image blur — its influence and ways to correct it. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W4, 33–39.
Şin, B., & Kadıoğlu, İ. (2019). İnsansız hava aracı (İHA) ve görüntü işleme teknikleri kullanılarak yabancı ot tespitinin yapılması. Turkish Journal of Weed Science, 22(2), 211–217.
Sönmez, N. K., Çoşlu, M., & Demir, N. (2021). Farklı insansız hava araçlarından (İHA) elde edilen veriler ile buğday bitkisinin boyunun belirlenmesi. Mediterranean Agricultural Sciences, 34(2), 195–203.
Ubben, N., Pukrop, M., & Jarmer, T. (2024). Spatial resolution as a factor for efficient UAV-based weed mapping: A soybean field case study. Remote Sensing, 16(10), 1778.
Woebbecke, D. M., Meyer, G. E., Von Bargen, K., & Mortensen, D. A. (1995). Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE, 38(1), 259–269.
Yıldırım Dalkıran, F., & Yıldırım, F. R. (2021). Uçağın trim parametresinin ANFIS kullanılarak tahmin edilmesi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 10(1), 54–62.
Yıldırım, F. R., & Yıldırım Dalkıran, F. (2026). Tarımda insansız hava araçlarının kullanımının incelenmesi. In ASES II. International Cappadocia Scientific Research Congress (pp. 20–27). Nevşehir, Türkiye.

Details

Primary Language

English

Subjects

Aerospace Engineering (Other)

Journal Section

Research Article

Authors

Fadiş Rabia Yıldırım ^*
0000-0003-4168-101X
Türkiye

Fatma Yıldırım Dalkıran
0000-0001-8663-241X
Türkiye

Mehmet Konar
0000-0002-9317-1196
Türkiye

Early Pub Date

October 22, 2026

Publication Date

October 25, 2026

Submission Date

August 20, 2026

Acceptance Date

September 28, 2026

Published in Issue

Year 2026 Volume: 10 Number: 3

DOI

https://doi.org/10.30518/jav.2022560

IZ

https://izlik.org/JA28TW67EB

Cite

RIS / Bibtex

APA

Yıldırım, F. R., Yıldırım Dalkıran, F., & Konar, M. (2026). Effect of UAV Flight Altitude on Wheat Field Classification Accuracy and Operational Efficiency. Journal of Aviation, 10(3), 461-469. https://doi.org/10.30518/jav.2022560

AMA

1.Yıldırım FR, Yıldırım Dalkıran F, Konar M. Effect of UAV Flight Altitude on Wheat Field Classification Accuracy and Operational Efficiency. JAV. 2026;10(3):461-469. doi:10.30518/jav.2022560

Chicago

Yıldırım, Fadiş Rabia, Fatma Yıldırım Dalkıran, and Mehmet Konar. 2026. “Effect of UAV Flight Altitude on Wheat Field Classification Accuracy and Operational Efficiency”. Journal of Aviation 10 (3): 461-69. https://doi.org/10.30518/jav.2022560.

EndNote

Yıldırım FR, Yıldırım Dalkıran F, Konar M (October 1, 2026) Effect of UAV Flight Altitude on Wheat Field Classification Accuracy and Operational Efficiency. Journal of Aviation 10 3 461–469.

IEEE

[1]F. R. Yıldırım, F. Yıldırım Dalkıran, and M. Konar, “Effect of UAV Flight Altitude on Wheat Field Classification Accuracy and Operational Efficiency”, JAV, vol. 10, no. 3, pp. 461–469, Oct. 2026, doi: 10.30518/jav.2022560.

ISNAD

Yıldırım, Fadiş Rabia - Yıldırım Dalkıran, Fatma - Konar, Mehmet. “Effect of UAV Flight Altitude on Wheat Field Classification Accuracy and Operational Efficiency”. Journal of Aviation 10/3 (October 1, 2026): 461-469. https://doi.org/10.30518/jav.2022560.

JAMA

1.Yıldırım FR, Yıldırım Dalkıran F, Konar M. Effect of UAV Flight Altitude on Wheat Field Classification Accuracy and Operational Efficiency. JAV. 2026;10:461–469.

MLA

Yıldırım, Fadiş Rabia, et al. “Effect of UAV Flight Altitude on Wheat Field Classification Accuracy and Operational Efficiency”. Journal of Aviation, vol. 10, no. 3, Oct. 2026, pp. 461-9, doi:10.30518/jav.2022560.

Vancouver

1.Fadiş Rabia Yıldırım, Fatma Yıldırım Dalkıran, Mehmet Konar. Effect of UAV Flight Altitude on Wheat Field Classification Accuracy and Operational Efficiency. JAV. 2026 Oct. 1;10(3):461-9. doi:10.30518/jav.2022560