A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data

Sensors (Switzerland)

Volumn 18, Issue 1, 2018, Pages

  Vanegas, Fernando ^a   Bratanov, Dmitry ^a   Powell, Kevin ^b,c,e   Weiss, John ^c,d   Gonzalez, Felipe ^a,c  

^a QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^b AGRICULTURE VICTORIA RESEARCH   (Australia)

^c PLANT BIOSECURITY COOPERATIVE RESEARCH CENTRE   (Australia)

^d LA TROBE UNIVERSITY   (Australia)

^e BSES LIMITED   (Australia)

Author keywords

Digital elevation model; Digital vigour assessment; Hyperspectral; Multispectral; Phylloxera; Remote sensing; RGB; Unmanned aerial vehicle

Indexed keywords

AIRCRAFT DETECTION; CROPS; IMAGE PROCESSING; MONITORING; SPECTROSCOPY; UNMANNED AERIAL VEHICLES (UAV);

DIGITAL ELEVATION MODEL; DIGITAL VIGOUR ASSESSMENT; HYPERSPECTRAL; MULTI-SPECTRAL; PHYLLOXERA;

REMOTE SENSING;

EID: 85040929196     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s18010260     Document Type: Article

References (31)

1. Mulla, D.J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng. 2013, 114, 358-371.
2. Marciniak, M. Use of remote sensing to understand the terroir of the Niagara Peninsula. Applications in a Riesling vineyard. J. Int. Sci. Vigne Vin 2015, 49, 1-26.
3. Mathews, A.J. A Practical UAV Remote Sensing Methodology to Generate Multispectral Orthophotos for Vineyards: Estimation of Spectral Reflectance. Int. J. Appl. Geospat. Res. 2015, 6, 65-87.
4. Zhang, C.; Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: A review. Precis. Agric. 2012, 13, 693-712.
5. Blanchfield, A.L.; Robinson, S.A.; Renzullo, L.J.; Powell, K.S. Phylloxera-infested grapevines have reduced chlorophyll and increased photoprotective pigment content-Can leaf pigment composition aid pest detection? Funct. Plant Biol. 2006, 33, 507-514.
6. Skinkis, P.; Walton, V.; Kaiser, C. Grape Phylloxera, Biology and Management in the Pacific Northwest; Oregon State University, Extension Service: Corvallis, OR, USA, 2009; pp. 7-9.
7. Benheim, D.; Rochfort, S.; Robertson, E.; Potter, I.D.; Powell, K.S. Grape phylloxera (Daktulosphaira vitifoliae)-A review of potential detection and alternative management options. Ann. Appl. Biol. 2012, 161, 91-115.
8. Powell, K. A Holistic Approach to Future Management of Grapevine Phylloxera; Springer: Dordrecht, The Netherlands, 2012; ISBN 9789400740327.
9. Powell, K.S.; Korosi, G.A.; Mackie, A.M. Monitoring grape phylloxera populations using simple nondestructive trapping systems. Acta Hortic. 2009, 816, 29-34.
10. Giblot-Ducray, D.; Correll, R.; Collins, C.; Nankivell, A.; Downs, A.; Pearce, I.; Mckay, A.C.; Ophel-Keller, K.M. Detection of grape phylloxera (Daktulosphaira vitifoliae Fitch) by real-time quantitative PCR: Development of a soil sampling protocol. Aust. J. Grape Wine Res. 2016, 22, 469-477.
11. Renzullo, L.J.; Blanchfield, A.L.; Powell, K.S. Insights into the early detection of grapevine phylloxera from in situ hyperspectral data. Acta Hortic. 2007, 733, 59-74.
12. Wildman, W.E.; Nagaoka, R.T.; Lider, L.A. Monitoring spread of grape phylloxera by color infrared aerial photography and ground investigation. Am. J. Enol. Vitic. 1983, 34, 83-94.
13. Lobitz, B.; Johnson, L.; Hlavka, C.; Armstrong, R.; Bell, C. Grapevine Remote Sensing Analysis of Phylloxera Early Stress (GRAPES): Remote Sensing Analysis Summary; NASA Ames Research Center: Mountain View, CA, USA, 1997; pp. 1-36.
14. Powell, K.S.; Hackworth, P.; Lewis, M.; Lamb, D.W.; Edwards, J. Identification of phylloxera from high resolution infrared aerial imagery: A comparative study between airborne imagery types. Aust. N. Z. Grapegrow. Winemak. 2004, 488, 51-54.
15. Renzullo, L.J. Characterizing grapevine spectral response to phylloxera infestation. Boll. Comunità Sci. Australas. 2004, 1, 28-32.
16. Frazier, P.; Whiting, J.; Powell, K.; Lamb, D. Characterising the development of grape phylloxera infestation with multi-temporal near-infrared aerial photography. Aust. N. Z. Grapegrow. Winemak. 2004, 485, 133-136.
17. Bellvert, J.; Zarco-Tejada, P.J.; Girona, J.; Fereres, E. Mapping crop water stress index in a Pinot-noir vineyard:Comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precis. Agric. 2014, 15, 361-376.
18. Mathews, A.J. Object-based spatiotemporal analysis of vine canopy vigor using an inexpensive unmanned aerial vehicle remote sensing system. J. Appl. Remote Sens. 2014, 8, 085199.
19. Bachmann, F.; Herbst, R.; Gebbers, R.; Hafner, V.V. Micro Uav Based Georeferenced Orthophoto Generation in Vis + Nir for Precision Agriculture. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2013, XL-1/W2, 11-16.
20. Honkavaara, E.; Kaivosoja, J.; Pellikka, I.; Pesonen, L.; Saari, H.; Salo, H.; Hakala, T.; Marklelin, L.; Solutions, M. Hyperspectral Reflectance Signatures and Point Clouds for Precision Agriculture by Light Weight Uav Imaging System. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2012, I, 353-358.
21. Honkavaara, E.; Saari, H.; Kaivosoja, J.; Pölönen, I.; Hakala, T.; Litkey, P.; Mäkynen, J.; Pesonen, L. Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote Sens. 2013, 5, 5006-5039.
22. De Castro, A.I.; Ehsani, R.; Ploetz, R.C.; Crane, J.H.; Buchanon, S. Detection of laurel wilt disease in avocado using low altitude aerial imaging. PLoS ONE 2015, 10, 1-13.
23. Psirofonia, P.; Samaritakis, V.; Eliopoulos, P.; Potamitis, I. Use of Unmanned Aerial Vehicles for Agricultural Applications with Emphasis on Crop Protection: Three Novel Case-Studies. Int. J. Agric. Sci. Technol. 2017, 5, 30-39.
24. Habili, N.; Oorloff, J. Scyllarus: From Research to Commercial Software. In Proceedings of the ASWEC 2015 24th Australasian Software Engineering Conference, Adelaide, Australia, 28 September-1 October 2015; pp. 119-122.
25. Rouse, J.W.; Haas, R.H.; Schell, J.A. Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation; NASA’s Goddard Space Flight Center: Greenbelt, MD, USA, 1974; pp. 1-8.
26. Rodriguez, D.; Fitzgerald, G.J.; Belford, R.; Christensen, L.K. Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts. Aust. J. Agric. Res. 2006, 57, 781-789.
27. Daughtry, C. Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sens. Environ. 2000, 74, 229-239.
28. Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.J.; Strachan, I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 2004, 90, 337-352.
29. Haboudane, D.; Miller, J.R.; Tremblay, N.; Zarco-Tejada, P.J.; Dextraze, L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 2002, 81, 416-426.
30. Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 1996, 55, 95-107.
31. Zarco-Tejada, P.J.; Berjón, A.; López-Lozano, R.; Miller, J.R.; Martín, P.; Cachorro, V.; González, M.R.; De Frutos, A. Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens. Environ. 2005, 99, 271-287.