Towards collaboration between unmanned aerial and ground vehicles for precision agriculture

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 10218, Issue , 2017, Pages

  Bhandari, Subodh ^a   Raheja, Amar ^a   Green, Robert L ^b   Do, Dat ^a  

^a Dept of Aerospace Engineering ^*  (United States)

^b CALIFORNIA STATE POLYTECHNIC UNIVERSITY   (United States)

Author keywords

Precision Agriculture; Remote Sensing; UAV UGV Collaboration

Indexed keywords

AGRICULTURAL CHEMICALS; AGRICULTURE; CAMERAS; CROPS; IMAGE PROCESSING; INTEGRATION TESTING; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; REMOTE SENSING; UNMANNED AERIAL VEHICLES (UAV); VEHICLES;

EXPERIMENTAL TESTING; GROUND TRUTHING; HUMAN EXPOSURES; HUMAN INTERVENTION; PRECISION AGRICULTURE; PROCESSED IMAGES; SENSOR INTEGRATION; UNMANNED GROUND VEHICLES;

GROUND VEHICLES;

EID: 85021795805     PISSN: 0277786X     EISSN: 1996756X     Source Type: Conference Proceeding    
DOI: 10.1117/12.2262049     Document Type: Conference Paper

References (28)

1. Bhandari, S., Tang, D., Boskovich, S., Zekeriya, A., Demonteverde, R., et al., "Collaboration between Multiple Unmanned Vehicles for Increased Mission Efficiency," Proceedings of Infotech@Aerospace Conference, San Diego, CA, (2016).
2. Aguera, F., Carvajal, F., et al., "Measuring sunflower nitrogen status from an unmanned aerial vehicle-based system and an on the ground device," Conference on Unmanned Aerial Vehicle in Geomatics, UAV-g-2011, Zurich, Switzerland, (2011).
3. Herwitz, S.R., Johnson, L.F., et al., "Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support," Computers and Electronics in Agriculture, vol. 44, pp. 49-61 (2004).
4. Abuleli, A.M., Taylor, G.W., and Moussa, M., "An integrated system for mapping red clover ground cover using unmanned aerial vehicles: A Case Study in Precision Agriculture," IEEE Conference on Computer and Robot Vision, Halifax, Nova Scotia, (2015).
5. Tokekar, P., Hook, J. V., et al., "Sensor Planning for a Symbiotic UAV and UGV System for Precision Agriculture," IEEE Transactions on Robotics, Vol. 32, No. 6, (2016).
6. Vasudevan, A., Kuma, A., and Bhuvaneswari, N. S., "Precision farming using unmanned aerial and ground vehicles," IEEE International Conference on Technological Innovations in ICT for Agriculture and Rural Development
7. Casady, W.W., "Precision Agriculture: Remote Sensing and Ground Truthing," MU Extension, University of Missouri-Columbia.
8. Dimitriadis, S. and Goumopoulos, C., "Applying machine learning to extract new knowledge in precision agriculture Applications," IEEE Panhellenic Conference on Informatics, Samos, Greece, 28-30 Aug. (2008).
9. Ding, K., Raheja, A., and Bhandari, S., "Application of machine learning for the evaluation of turfgrass plots using aerial images," Proceedings of SPIE Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping Conference, Baltimore, MD, (2016).
10. Sona, G., Passoni, D., et al., "UAV multispectral survey to map soil and crop for precision farming applications," XXIII ISPRS Congress, Prague, Czech Republic, (2016).
11. Rascher, U., Nichols, C. J., and Small, C., "Hyperspectral imaging of photosynthesis from the single leaf to the complex canopy-understanding the spatio-temporal variations of photosynthesis within a drought-stressed tropical canopy," Imaging Spectroscopy: New Quality in Environmental Studies, pp. 709-719, (2006).
12. Franke, J., Mewes, T., and Menz, G., "Airborne hyperspectral imaging for the detection of powdery mildew in wheat, proceedings of SPIE optics and photonics, Vol. 7086, San Diego, (2008).
13. Govender, M., Chetty, K. et al., "A comparison of satellite hyperspectral and multispectral remote sensing imagery for improved classification and mapping of vegetation, Water SA, Vol. 34, No. 2, pp. 147-154, (2008).
14. http://www.headwallphotonics.com/spectral-imaging/complementary-technologies/hyperspec-software.
15. Habib, A., Xiong, W., et al., "Improving orthorectification of UAV-based push-broom scanner imagery using derived orthophotos from frame cameras, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10, No. 1, (2016).
16. Habib, A., Han, Y., et al., "Automated ortho-rectification of UAV-based hyperspectral data over an agricultural field using frame RGB imagery," Remote Sensing, Vol. 8, No. 10, (2016).
17. Heid, M., Bettadapura, A., Ito, E., Bhandari, S., and Tang, D., "A ground control station for multivehicular control and data visualization," Proceedings of AIAA Infotech@Aerospace Conference, Kissimmee, FL, (2015).
18. Lelong, C.C.D., Burger, P., et al., "Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots," Sensors, Vol. 8, pp. 3557-3585, (2008).
19. Al-Jabri, S. A., Lee, J., et al., "A dripper-TDR method for in situ determination of hydraulic conductivity and chemical transport properties of surface soils," Advances in Water Resources, Vol. 29, No. 2, pp. 239-249, (2006).
20. Congalton, R.G. and Green, K., "Assessing the accuracy of remotely sensed data: principles and practices," Taylor and Francis: Boca Raton, FL, USA, (2009).
21. Govendor, M., Dye, P.J., et al., "Review of commonly used remote sensing and ground-based technologies to measure plant water stress," Review, Water SA, Vol. 35, No. 5, pp. 741-752, (2009).
22. Lindsey, A. J., Steinke, K., et al., "Relationship between DGCI and SPAD values to corn grain yield in the eastern corn belt," Crop, Forage, and Turf Management (2016).
23. Hunt, E. R., and Daughtry, C. S. T., "Chlorophyll meter calibrations for chlorophyll content using measured and simulated leaf transmittances," Agronomy Journal, Vol. 106, No. 3, pp. 931-939, (2014).
24. Fox, R. H. and Walthall, C. L., "Crop monitoring technology to assess nitrogen status," Nitrogen in Agricultural Systems, American Society of Agronomy, Agronomy Monograph No. 49, pp. 647-674, (2008).
25. Ioffe, S. and Szegedy, C., "Batch normalization: accelerating deep network training by reducing internal covariate shift," Proceedings of the 32nd International Conference on Machine Learning, Lille, France, (2015).
26. Kingma, D. and Ba, J., "Adam: a method for stochastic optimization," International Conference on Learning Representations, (2015).
27. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., amd Salakhutdinov, R., "Dropout: a simple way to prevent neural networks from overfitting," Journal of Machine Learning Research, Vol 15, pp 1929-1958, (2014).
28. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z., "Rethinking the inception architecture for computer vision," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (2016).