Lessons Learned from UAV-Based Remote Sensing for Precision Agriculture ∗

2018 International Conference on Unmanned Aircraft Systems, ICUAS 2018

Volumn , Issue , 2018, Pages 458-467

  Bhandari, Subodh ^a   Raheja, Amar ^a   Chaichi, Mohammad R ^a   Green, Robert L ^a   Do, Dat ^a   Pham, Frank H ^a   Ansari, Mehdi ^a   Wolf, Joseph G ^a   Sherman, Tristan M ^a   Espinas, Antonio ^a  

^a CALIFORNIA STATE POLYTECHNIC UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AIRCRAFT DETECTION; FORESTRY; PRECISION AGRICULTURE; TESTING; UNMANNED AERIAL VEHICLES (UAV); VEGETATION;

CHLOROPHYLL METERS; DATA COLLECTION METHOD; NITROGEN STRESS; REMOTE SENSING DATA; REMOTE SENSING TECHNOLOGY; SPECTRAL SENSORS; SPECTRO-RADIOMETERS; VEGETATION INDEX;

REMOTE SENSING;

EID: 85053881896     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICUAS.2018.8453445     Document Type: Conference Paper

References (30)

1. T. Sherman, S. Bhandari, et al., "Cooperative Search and Rescue using Autonomous Unmanned Aerial Vehicles, " Proceedings of AIAA SciTech Forum, Kissimmee, FL, 8-12 Jan. 2018.
2. S. Bhandari, A. Bettadapura, et al., "Search and Rescue using Unmanned Aerial Vehicles, " Proceedings of AIAA Infotech@Aerospace Conference, Kissimmee, FL, 5-9 January 2015.
3. C. Carreon-Limones, S. Bhandari, et al., "Increasing the Accuracy of 3-D Maps Created using a UAV-Based LIDAR, " Proceedings AIAA SciTech Forum, Kissimmee, FL, 8-12 Jan. 2018.
4. Growing use of Drones Poised to Transform Agriculture, http: //www. usatoday. com/story/money/business/2014/03/23/dronesagriculture-growth/6665561/.
5. E. R. Hunt and W. D. Hively, et al., "Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring, " Remote Sensing, Vol. 2, pp. 290-305, 2010.
6. S. Nebiker, A. Annen, et al., "A Light-Weight Multispectral Sensor for Micro-UAV-Opportunities for Very High Resolution Airborne Remote Sensing, " International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume 37, Part B1, pp. 1193-1199, 2008.
7. E. R. Hunt, M. Cavigelli, et al., Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status, " Precision Agriculture, Vol. 6, No. 4, pp. 359-378, 2005.
8. R. P., Breckenridge and M. E. Dakins, Evaluation of Bare Ground on Rangelands using Unmanned Aerial Vehicles, " GIScience and Remote Sensing, Vol. 48, No. 1, pp. 74-85, 2011.
9. F. Aguera, F. Carvajal, 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.
10. 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.
11. K. C. Swain, S. J. Thomson, and H. P. W. Jayasuriya, "Adoption of an Unmanned Helicopter for Low-Altitude Remote Sensing to Estimate Yield and Total Biomass of a Rice Crop, " Transactions of ASABE, Vol. 53, pp. 21-27, 2010.
12. E. R. Hunt and W. D. Hively, "NIR-Green-Blue High-Resolution Digital Images for Assessment of Winter Crop Cover Biomass, " GIScience Remote Sensing, Vol. 48, pp. 86-98, 2011.
13. B. Bricco, R. J. Brown, et al., "Precision Agriculture and the Role of Remote Sensing: A Review, " Canadian Journal of Remote Sensing, Vol. 24, pp. 315-327, 1998.
14. W. W. Casady, "Precision Agriculture: Remote Sensing and Ground Truthing, " MU Extension, University of Missouri-Columbia.
15. K. C. Swain, H. P. W. Jayasuriya, and V. M., Salokhe, "Suitability of Low-Altitude Remote Sensing Images for Estimating Nitrogen Treatment Variations in Rice Cropping for Precision Agriculture Adoption, " Journal of Applied Remote Sensing, Vol. 1, 013547, 2007.
16. P. S. Fouché and N. W. Booysen, "Assessment of Crop Stress Conditions using Low Altitude Aerial Color-Infrared Photography and Computer Image Processing, " Geocarto International, Vol. 2, pp. 25-31, 1994.
17. S. Bhandari, A. Raheja, M. Chaichi, R. Green, D. Do, M. Ansari, et al., "Ground-Truthing of UAV-based Remote Sensing Data of Citrus Plants, " Proceedings of Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III Conference, Orlando, FL, 15-19 Apr. 2018.
18. S. Bhandari., A. Raheja, and R. Green, "Towards Collaboration between Unmanned Aerial and Ground Vehicles for Precision Agriculture, " Proceedings of Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, Anaheim, CA, 9-13 Apr. 2017.
19. S. Bhandari, A. Raheja, M. Chaichi, R. Green, D. Do, M. Ansari, et al., "Effectiveness of UAV-Based Remote Sensing Techniques in Determining Lettuce Nitrogen and Water Stresses, " Accepted for Presentation at and Publication in the Proceedings of International Conference on Precision Agriculture, Montreal, Canada, 24-27 June 2018.
20. D. Dat, F. Pham, A. Raheja, and S. Bhandari, "Machine Learning Techniques for the Assessment of Citrus Plant Health using UAVbased Digital Images, " Proceedings of Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III Conference, Orlando, FL, 15-19 Apr. 2018.
21. C. Phan, A. Raheja, S. Bhandari, et al., "A Predictive Model for Turfgrass Color and Quality Evaluation using Deep Learning and UAV Imageries, " Proceedings of Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, Anaheim, CA, 9-13 Apr. 2017.
22. K. Ding, A. Raheja, and S. Bhandari, "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, 17-19 Apr. 2016.
23. A. Habib, W. Xiong, et al., "Improving Orthorectification of UAVbased 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.
24. A. Habib, A., Y. Han, et al., "Automated Ortho-Rectification of UAVbased Hyperspectral Data Over an Agricultural Field using frame RGB imagery, " Remote Sensing, Vol. 8, No. 1, 2016.
25. M. Govendor, P. J. Dye, 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.
26. A. J. Lindsey, K. Steinke, et al., "Relationship between DGCI and SPAD Values to Corn Grain Yield in the Eastern Corn Belt, " Crop, Forage, and Turf Management 2016.
27. R. G. Congalton and K. Green, "Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, " Taylor and Francis: Boca Raton, FL, USA, 2009.
28. S. A. Al-Jabri, J. Lee, 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.
29. G. Fady, and S. Bhandari., "Design of Robust Controllers for UAV Helicopters using Quantitative Feedback Theory, " Infotech@Aerospace, Garden Grove, CA, 19-21 June 2012.
30. N. Patel and S. Bhandari., "Nonlinear Adaptive Control of a Fixed-Wing UAV using Multilayer Perceptrons, " Proceedings of AIAA Guidance, Navigation, and Control Conference, Grapevine, TX, 9-13 Jan. 2017.