Precision Agricultural Systems - Agricultural Automation - page 93

Tumbo, S. D., M. Salyani, W. M. Miller, R. Sweeb, and S. Buchanon. 2007. Evaluation of
a variable rate controller for aldicarb application around buffer zone in citrus groves.
Computers and Electronics in Agriculture 56: 147-160.
U.S. Geological Survey. 2011. Geographic information systems. Available at egsc.usgs.gov/
isb/pubs/gis_poster. Accessed October 13, 2011.
Varvel G. E., M. R. Schlemmer, and J. S. Schepers. 1999. Relationship between spectral
data from an aerial image and soil organic matter and phosphorus levels. Precision
Agriculture 1: 291-300.
Vellidis, G., M. Tucker, C. Perry, C. Kvien, and C. Bednarz. 2007. A real-time wireless smart sen-
sor array for scheduling irrigation. Computers and Electronics in Agriculture 61: 44-50.
Vinnikov, K. Y., Robock, A., Qiu, S., Entin, J. K., Owe, M., Choudhury, B. J., Hollinger, S. E.,
Njoku, E. G., 1999. Satellite Remote Sensing of Soil Moisture in Illinois, USA. Journal
of Geophysical Research 104(D4): 4145-4168.
Wagner, L. E., and M. D. Schrock. 1989. Yield determination using a pivoted auger flow sen-
sor. Transactions of the ASAE 32(2): 409-413.
Walvoort, D. J. J., and A. B. McBratney. 2001. Diffuse reflectance spectrometry as a proximal
sensing tool for precision agriculture. In Grenier, G., and Blackmore S. (eds.), Proc.
Third European Conference on Precision Agriculture , ECPA 2001, Montpellier, France,
pp. 503-507.
Wang, N., N. Zhang, and M. Wang. 2006. Wireless sensors in agriculture and food industry:
Recent development and future perspective. Computers and Electronics in Agriculture
50(1): 1-14.
Wendroth O., G. Schwab, D. Egli, S. Kumudini, T. Mueller, and L. Murdock. 2011. In-season
observation of wheat growth status for yield prediction: do different optical sensors
give us the same answer? Available at: http://www.ca.uky.edu/ukrec/RR%202006-07/
RR06-07%20pg64.pdf. Accessed October 3, 2011.
Whelan, B. M., and A. B. McBratney. 1997. Sorghum grain flow convolution within a conven-
tional combine harvester. In Proc. 1st European Conference on Precision Agriculture, Vol II:
Technology, IT and Management , 759-766. Oxford, England: BIOS Scientific Publishers.
Yang, C. 2010. A high resolution airborne four-camera imaging system for agricultural appli-
cations. ASABE Paper No. 1008856. St. Joseph MI: ASABE.
Yang, C. 2001. A variable rate applicator for controlling rates of two liquid fertilizers. Applied
Engineering in Agriculture 17(3): 409-417.
Yang, C., and G. L. Anderson. 1999. Airborne videography to identify spatial plant growth
variability for grain sorghum. Precision Agriculture 1(1): 67-79.
Yang, C., and J. H. Everitt. 2000. Relationships between yield monitor data and airborne
multidate multispectral digital imagery for grain sorghum. Precision Agriculture 3(4):
373-388.
Yang, C., J. H. Everitt, and J. M. Bradford. 2007. Airborne hyperspectral imagery and lin-
ear spectral unmixing for mapping variation in crop yield. Precision Agriculture 8(6):
279-296.
Yang, C., J. H. Everitt, and J. M. Bradford. 2006. Evaluating of high resolution QuickBird sat-
ellite imagery for estimating cotton yield. Transactions of the ASABE 49(5): 1599-1606.
Yang, C., J. H. Everitt, and J. M. Bradford. 2002. Optimum time lag determination for yield
monitoring with remotely sensed imagery. Transactions of the ASAE 45(6): 1737-1745.
Yang, C., J. H. Everitt, J. M. Bradford, and D. Murden. 2004. Airborne hyperspectral imagery and
yield monitor data for mapping cotton yield variability. Precision Agriculture 5(5): 445-461.
Yang, C., J. H. Everitt, M. R. Davis, and C. Mao. 2003. A CCD camera-based hyperspectral imag-
ing system for stationary and airborne applications. Geocarto International 18(2): 71-80.
Yang, C., J. H. Everitt, and Q. Du. 2010a. Applying linear spectral unmixing to airborne
hyperspectral imagery for mapping yield variability in grain sorghum and cotton fields.
Journal of Applied Remote Sensing 4: 041887.