Practical applications of a multisensor UAV platform based on multispectral, thermal and RGB high resolution images in precision viticulture

Agriculture (Switzerland)

Volumn 8, Issue 7, 2018, Pages

  Matese, Alessandro ^a   Di Gennaro, Salvatore Filippo ^a  

^a INSTITUTE OF BIOMETEOROLOGY   (Italy)

Author keywords

CWSI; High resolution images; Missing plants; Multisensor; UAV

Indexed keywords

EID: 85050593223     PISSN: None     EISSN: 20770472     Source Type: Journal    
DOI: 10.3390/agriculture8070116     Document Type: Article

References (33)

1. Arnó, J.; Martínez-Casasnovas, J.A.; Ribes-Dasi, M.; Rosell, J.R. Review. Precision Viticulture. Research topics, challenges and opportunities in site-specific vineyard management. Span. J. Agric. Res. 2009, 7, 779–790.
2. Matese, A.; Di Gennaro, S.F.; Berton, A. Assessment of a Canopy Height Model (CHM) in a vineyard using UAV-based multispectral imaging. Int. J. Remote Sens. 2017, 38, 8–10. [CrossRef]
3. Matese, A.; Di Gennaro, S.F.; Miranda, C.; Berton, A.; Santesteban, L. Evaluation of spectral-based and canopy-based vegetation indices from UAV and Sentinel 2 images to assess spatial variability and ground vine parameters. Adv. Anim. Biosci. 2017, 8, 817–822. [CrossRef]
4. Di Gennaro, S.F.; Matese, A.; Gioli, B.; Toscano, P.; Zaldei, A.; Palliotti, A.; Genesio, L. Multisensor approach to assess vineyard thermal dynamics combining high-resolution unmanned aerial vehicle (UAV) remote sensing and wireless sensor network (WSN) proximal sensing. Sci. Hortic. 2017, 221, 83–87. [CrossRef]
5. Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the great plains with ERTS. In Proceedings of the 3rdEarth Resources Technology Satellite Symposium, Ottawa, ON, Canada, 10–14 December 1973; pp. 309–317.
6. Goel, N.S. Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data. Remote Sens. Rev. 1988, 4, 1–212. [CrossRef]
7. Jacquemoud, S.; Bacour, C.; Poilve, H.; Frangi, J.P. Comparison of four radiative transfer models to simulate plant canopies reflectance: Direct and inverse model. Remote Sens. Environ. 2000, 74, 417–481. [CrossRef]
8. Zarco-Tejada, P.J.; Miller, J.R.; Noland, T.L.; Mohammed, G.H.; Sampson, P.H. Scaling-up and model inversion methods with narrow band optical indices for chlorophyll content estimation in closed forest canopies with hyper-spectral data. IEEE Trans. Geosci. Remote Sens. 2001, 39, 1491–1507. [CrossRef]
9. Hall, A.; Lamb, D.W.; Holzapfel, B.; Louis, J. Optical remote sensing applications for viticulture—A review. Aust. J. Grape Wine Res. 2002, 8, 36–47. [CrossRef]
10. Hall, A.; Louis, J.; Lamb, D. Characterising and mapping vineyard canopy using high-spatial-resolution aerial multispectral images. Comput. Geosci. 2003, 29, 813–822. [CrossRef]
11. Fiorillo, E.; Crisci, A.; De Filippis, T.; Di Gennaro, S.F.; Di Blasi, S.; Matese, A.; Primicerio, J.; Vaccari, F.P.; Genesio, L. Airborne high-resolution images for grape classification: Changes in correlation between technological and late maturity in a Sangiovese vineyard in Central Italy. Aust. J. Grape Wine Res. 2012, 18, 80–90. [CrossRef]
12. Filippetti, I.; Allegro, G.; Valentini, G.; Pastore, C.; Colucci, E.; Intrieri, C. Influence of vigour on vine performance and berry composition of cv. Sangiovese (Vitis vinifera L.). J. Int. Sci. Vigne Vin 2013, 47, 21–33. [CrossRef]
13. Gatti, M.; Garavani, A.; Vercesi, A.; Poni, S. Ground-truthing of remotely sensed within-field variability in a cv. Barbera plot for improving vineyard management. Aust. J. Grape Wine Res. 2017, 23, 399–408. [CrossRef]
14. Johnson, L.F.; Roczen, D.E.; Youkhana, S.K.; Nemani, R.R.; Bosch, D.F. Mapping vineyard leaf area with multispectral satellite imagery. Comput. Electron. Agric. 2003, 38, 33–44. [CrossRef]
15. Berni, J.A.; Zarco-Tejada, P.J.; Suárez, L.; Fereres, E. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sens. 2009, 47, 722–738. [CrossRef]
16. Matese, A.; Toscano, P.; Di Gennaro, S.F.; Genesio, L.; Vaccari, F.P.; Primicerio, J.; Belli, C.; Zaldei, A.; Bianconi, R.; Gioli, B. Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sens. 2015, 7, 2971–2990. [CrossRef]
17. Santesteban, L.G.; Di Gennaro, S.F.; Herrero-Langreo, A.; Miranda, C.; Royo, J.B.; Matese, A. High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agric. Water Manag. 2017, 183, 49–59. [CrossRef]
18. Jones, H.G. Irrigation scheduling: Advantages and pitfalls of plant-based methods. J. Exp. Bot. 2004, 55, 2427–2436. [CrossRef] [PubMed]
19. Grant, O.M.; Tronina, L.; Jones, H.G.; Chaves, M.M. Exploring thermal imaging variables for the detection of stress responses in grapevine under different irrigation regimes. J. Exp. Bot. 2007, 58, 815–825. [CrossRef] [PubMed]
20. Möller, M.; Alchanatis, V.; Cohen, Y.; Meron, M.; Tsipris, J.; Naor, A.; Ostrovsky, V.; Sprintsin, M.; Cohen, S. Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. J. Exp. Bot. 2007, 58, 827–838. [CrossRef] [PubMed]
21. Gontia, N.K.; Tiwari, K.N. Development of crop water stress index of wheat crop for scheduling irrigation using infrared thermometry. Agric. Water Manag. 2008, 95, 1144–1152. [CrossRef]
22. Jones, H.G.; Serraj, R.; Loveys, B.R.; Xiong, L.; Wheaton, A.; Price, A.H. Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Funct. Plant. Biol. 2009, 36, 978–979. [CrossRef]
23. Romano, G.; Zia, S.; Spreer, W.; Sanchez, C.; Cairns, J.; Araus, J.L.; Müller, J. Use of thermography for high throughput phenotyping of tropical maize adaptation in water stress. Comput. Electron. Agric. 2011, 79, 67–74. [CrossRef]
24. Alchanatis, V.; Cohen, Y.; Cohen, S.; Moller, M.; Sprinstin, M.; Meron, M.; Tsipris, J.; Saranga, Y.; Sela, E. Evaluation of different approaches for estimating and mapping crop water status in cotton with thermal imaging. Precis. Agric. 2010, 11, 27–41. [CrossRef]
25. Rabatel, G.; Delenne, C.; Deshayes, M. A non-supervised approach using Gabor filters for vine plot detection in aerial images. Comput. Electron. Agric. 2008, 62, 159–168. [CrossRef]
26. Kelcey, J.; Lucieer, A. Sensor correction of a 6-band multispectral imaging sensor for UAV remote sensing. Remote Sens. 2012, 4, 1462–1493. [CrossRef]
27. Jones, H.G.; Vaughan, R.A. Remote Sensing of Vegetation: Principles, Techniques, and Applications; Oxford University Press: Oxford, UK, 2010; ISBN 9780199207794.
28. Idso, S.B.; Jackson, R.D.; Pinter, P.J.; Reginato, R.J.; Hatfield, J.L. Normalizing the stress degree day parameter for environmental variability. Agric. Meteorol. 1981, 24, 45–55. [CrossRef]
29. Jones, H.G.; Stoll, M.; Santos, T.; de Sousa, C.; Chaves, M.M.; Grant, O.M. Use of infrared thermography for monitoring stomatal closure in the field: Application to grapevine. J. Exp. Bot. 2002, 53, 2249–2260. [CrossRef] [PubMed]
30. Bonilla, I.; Martinez de Toda, F.; Martínez-Casasnovas, J.A. Vine vigour, yield and grape quality assessment by airborne remote sensing over three years: Analysis of unexpected relationships in cv. Tempranillo. Span. J. Agric. Res. 2015, 13, 1–8. [CrossRef]
31. Matese, A.; Baraldi, R.; Berton, A.; Cesaraccio, C.; Di Gennaro, S.F.; Duce, P.; Facini, O.; Mameli, M.G.; Piga, A.; Zaldei, A. Estimation of water stress in grapevines using proximal and remote sensing methods. Remote Sens. 2018, 10, 114. [CrossRef]
32. Poblete, T.; Ortega-Farías, S.; Ryu, D. Automatic coregistration algorithm to remove canopy shaded pixels in UAV-borne thermal images to improve the estimation of crop water stress index of a drip-irrigated Cabernet sauvignon vineyard. Sensors 2018, 18, 397. [CrossRef] [PubMed]
33. Delenne, C.; Durrieu, S.; Rabatel, G.; Deshayes, M. From pixel to vine parcel: A complete methodology for vineyard delineation and characterization using remote-sensing data. Comput. Electron. Agric. 2010, 70, 78–83. [CrossRef]