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Use of Unmanned Aerial Vehicle for Multi-temporal Monitoring of Soybean Vegetation Fraction
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  • Journal title : Journal of Biosystems Engineering
  • Volume 41, Issue 2,  2016, pp.126-137
  • Publisher : Korean Society for Agricultural Machinery
  • DOI : 10.5307/JBE.2016.41.2.126
 Title & Authors
Use of Unmanned Aerial Vehicle for Multi-temporal Monitoring of Soybean Vegetation Fraction
Yun, Hee Sup; Park, Soo Hyun; Kim, Hak-Jin; Lee, Wonsuk Daniel; Lee, Kyung Do; Hong, Suk Young; Jung, Gun Ho;
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Purpose: The overall objective of this study was to evaluate the vegetation fraction of soybeans, grown under different cropping conditions using an unmanned aerial vehicle (UAV) equipped with a red, green, and blue (RGB) camera. Methods: Test plots were prepared based on different cropping treatments, i.e., soybean single-cropping, with and without herbicide application and soybean and barley-cover cropping, with and without herbicide application. The UAV flights were manually controlled using a remote flight controller on the ground, with 2.4 GHz radio frequency communication. For image pre-processing, the acquired images were pre-treated and georeferenced using a fisheye distortion removal function, and ground control points were collected using Google Maps. Tarpaulin panels of different colors were used to calibrate the multi-temporal images by converting the RGB digital number values into the RGB reflectance spectrum, utilizing a linear regression method. Excess Green (ExG) vegetation indices for each of the test plots were compared with the M-statistic method in order to quantitatively evaluate the greenness of soybean fields under different cropping systems. Results: The reflectance calibration methods used in the study showed high coefficients of determination, ranging from 0.8 to 0.9, indicating the feasibility of a linear regression fitting method for monitoring multi-temporal RGB images of soybean fields. As expected, the ExG vegetation indices changed according to different soybean growth stages, showing clear differences among the test plots with different cropping treatments in the early season of < 60 days after sowing (DAS). With the M-statistic method, the test plots under different treatments could be discriminated in the early seasons of <41 DAS, showing a value of M > 1. Conclusion: Therefore, multi-temporal images obtained with an UAV and a RGB camera could be applied for quantifying overall vegetation fractions and crop growth status, and this information could contribute to determine proper treatments for the vegetation fraction.
Barley cover cropping;Excess green;Image processing;M-statistic method;UAV;Vegetation index;
 Cited by
Berni, J. A., P. J. Zarco-Tejada, L. Suarez and E. Fereres, 2009. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47(3):722-738. crossref(new window)

Corre-Hellou, G., A. Dibet, H. Hauggaard-Nielsen, Y. Crozat, M. G ooding, P . Ambus and E. S. Jensen. 2011. The competitive ability of pea-barley intercrops against weeds and the interactions with crop productivity and soil N availability. Field Crops Research 122(3):264-272. crossref(new window)

Garcia-Ruiz, F., S. Sankaran, J. M. Maja, W. S. Lee, J. Rasmussen and R. Ehsani. 2013. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Computers and Electronics in Agriculture 91:106-115. crossref(new window)

Hunt, E. R., W. D. Hively, S. J. Fujikawa, D. S. Linden, C.S. Daughtry and G.W. McCarty. 2010. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing 2(1):290-305. crossref(new window)

Jimenez, P. L. and D. Agudelo 2015. Validation and calibration of a high resolution sensor in unmanned aerial vehicles for producing images in the IR range utilizable in precision agriculture. In: Proceedings of the AIAA SciTech, Paper No. 2015-0988. Kissimmee, FL: AIAA Infotech @ Aerospace.

Kaufman, Y. J. and L. A. Remer. 1994. Detection of forests using mid-IR reflectance: an application for aerosol studies. IEEE Transactions on Geoscience and Remote Sensing 32(3):672-683. crossref(new window)

Kobayashi, H., S. Miura and A. Oyanagi. 2004. Effects of winter barley as a cover crop on the weed vegetation in a no-tillage soybean. Weed Biology and Management 4(4):195-205. crossref(new window)

Lu, Y. C., K. B. Watkins, J. R. Teasdale and A. A. Abdul-Bakil. 2000. Cover crops in sustainable food production. Food Reviews International 16(2):121-157. crossref(new window)

Poggio, S. L. 2005. Structure of weed communities occurring in monoculture and intercropping of field pea and barley. Agriculture, Ecosystems and Environment 109(1):48-58. crossref(new window)

Pena, J. M., J. Torres-Sanchez, A. I. de Castro, M. Kelly, and F. Lopez-Granados. 2013. Weed mapping in earlyseason maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS One 8(10):e77151. crossref(new window)

Pereira, J. M., A. C. Sa, A. M. Sousa, J. M. Silva, T. N. Santos, and J. M. Carreiras. 1999. Spectral characterization and discrimination of burnt areas. In: Remote sensing of large wildfires, pp. 123-138. Springer-Berlag Berlin Heidelberg.

Smith, G. M., and E. J. Milton. 1999. The use of the empirical line method to calibrate remotely sensed data to reflectance. International Journal of remote sensing, 20(13):2653-2662. crossref(new window)

Todd, A. P., C. B. Orvin, and H. O. James. 1999. Increasing crop competitiveness to weeds through crop breeding. Journal of Crop Production 2(1):59-76. crossref(new window)

Torres-Sanchez, J., J. M. Pena, A. I. de Castro, and F. Lopez-Granados. 2014. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture 103:104-113. crossref(new window)

Wang, C. and S. W. Myint, 2015. A simplified empirical line method of radiometric calibration for small unmanned aircraft systems-based remote sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5):1876-1885. crossref(new window)

Woebbecke, D. M., G. E. Meyer, K. Von Bargen, and D. A. Mortensen, 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE 38(1):259-269. crossref(new window)