Ongoing research on Unmanned Aerial Vehicles (UAVs) is aimed at determin¬ing the utility of UAVs for agricultural remote sensing applications. Aerial pho¬tography from unmanned aerial vehicles bridges the gap between ground-based observations and remotely sensed imagery from aerial and satellite platforms. In the present study, Crop area measurements are carried out by analysis of aerial imagery acquired through Low Altitude Remote Sensing (LARS) carried out using a Quadcopter UAV. The area per pixel or the Ground Separation Distance (GSD) is computed using the altitude measurements from a barometer. Image processing clus¬tering techniques are applied to classify non crop and crop area in the image extent. Further the physical crop area and non crop area is determined using GSD. In this study K-Means and Mean shift clustering techniques are used to classify crop and non crop area. Performance of determining crop area is compared for K-means and Mean shift techniques. The results indicate crop area classification using Meanshift outperforms classification using K-means.

Unmanned Aerial Vehicle, Low Altitude Remote Sensing, Crop Area Mapping, Image clustering

REFERENCES
[1] Colomina .I, Molina .P, “Unmanned aerial systems for photogrammetry and remote sensing”, A review ISPRS Journal of Photogrammetry and Remote Sensing 92, 7997 (2014)
[2] Swain Kishore Chandra, Jayasuriya, H.P.W. , “ Low Altitude Remote Sensing Applications for Diversified Farming Conditions in Developing Countries: An Overview”, AsiaPacific Journal of Rural Development. Vol. XVIII, No. 2, De¬cember 2008
[3] Herwitz S.R, Johnson L.F, Dunagan S.E, Higgins R.G, Sullivan D.V, Zheng J, Lob¬itz B.M , Leung .J, Gallmeyer B.A ,Aoyagi M, Slye R.E, Brass J.A, 2004, “Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support”, Computers and Electronics in Agriculture 44 4961, 2004
[4] Juan I. Corcoles, Jose F. Ortega, David Hernandez , Miguel A. Moreno, 2013, “Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle”, Biosystems Engineering 115(2013) 31-42, 2013
[5] Jose A. J. Berni, Pablo J. Zarco-Tejada, Surez Lola , and Fereres Elias, “Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle”, IEEE transactions on Geoscience and Remote Sensing , vol. 47, NO. 3, march 2009
[6] Suzuki Taro, Amano Yoshiharu, Jun-ichi Takiguchi, Hashizume Takumi, Suzuki Shinji and Yamaba Atsushi, “Development of Low-cost and Flexible Vegetation Monitoring System Using small Unmanned Aerial Vehicle”, ICROS-SICE International joint Conference, Fukuoka International Congress Center, Japan, 2009.
[7] Dugo Gonzalez .V , Zarco-Tejada .P, Nicols .E , Nortes P.A, Alarcn J.J, Intrigli¬olo D.S and Fereres .,”Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard”, International Journal on Advances in Precision Agriculture, 2013
[8] Swain, Kishore C., and Qamar Uz Zaman. "Rice crop monitoring with unmanned helicopter remote sensing images." Remote Sensing of Biomass-Principles and Applications. InTech, 2012.
[9] Carlson Tobu N and Riziley A.David, “ On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index”, REMOTE SENS. ENVIRON. 62241-252 1997.
[10] Selim S.Z. and Ismail M.A. , 1984 ,”K-means type algorithms: a generalized con-vergence theorem and characterization of local optimality”, IEEE Transactions on Pattern Analysis and Machine Intelligence 6 8187, 1984 8187
[11] Comaniciu, D. and Meer, P. “Mean shift: A robust approach toward feature space analysis”. IEEE Transactions on pattern analysis and machine intelligence, 24(5), pp.603-619, 2002
[12] Fawcett T ,”Roc graphs: notes and practical considerations for researchers”. Technical Report HPL-2003-4, HP Labs, 2006.