Amin U., 2017, A Survey on Precision Agriculture: Tech- nologies and Challenges, The 3rd International Conference on Next Generation Computing (ICNGC2017b), pp. 21-24

Costa F. G., Ueyama J., Braun T., Pessin G., Osório F. S., Vargas P. A., 2012, The use of unmanned aerial vehicles and wireless sensor network in agricultural applications, 2012 IEEE International Geoscience and Remote Sensing Symposium,, pp. 22-27

Freeman P. K., Freeland R. S., 2014, Politics & technology: US polices restricting unmanned aerial systems in agriculture, Food Policy, Vol. 49, No. 1, pp. 302-311

Kwak J. T., Ha J. G., Im S. H., Shim S. H., Kim E., Lee O. N., Park H, Y., Moon H. J., 2017, Low-altitude Imagery of Unmanned Aerial Vehicles for Radish Monitoring: A Pilot Study, ICEIC 2017 International Conference on Electronics, pp. 933-934

Ha J. G., Moon H. J., Kwak J. T., Syed I. H., Dang L. M., Lee O. N., Park H. Y., 2017, Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles, Journal of Applied Remote Sensing, Vol. 11, No. 4

Williams P. C., Norris K., 1987, Near-infrared technology in the agricultural and food industries, American Association of Cereal Chemists

Rouse Jr W. John, 1972, Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation, Texas A & M Univ

Yengoh G. T., Dent D., Olsson L., Tengberg A. E., Tucker C. J., 2016, Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales, Springer

Dang L. M., Syed I. H., Im S. H., Irfan M., Moon H. J., 2018, Utilizing text recognition for the defects extraction in sewers CCTV inspection videos, Computers in Industry, Vol. 99, pp. 96-109