Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-25T05:06:37.287Z Has data issue: false hasContentIssue false

A systemic approach to identify relevant information provided by UAV in precision viticulture

Published online by Cambridge University Press:  01 June 2017

L. Pichon*
Affiliation:
UMR ITAP, Montpellier SupAgro/Irstea, building 21, 2 place Pierre Viala, 34 060 Montpellier, France
G. Besqueut
Affiliation:
UMR ITAP, Montpellier SupAgro/Irstea, building 21, 2 place Pierre Viala, 34 060 Montpellier, France
B. Tisseyre
Affiliation:
UMR ITAP, Montpellier SupAgro/Irstea, building 21, 2 place Pierre Viala, 34 060 Montpellier, France
*
Get access

Abstract

By providing high spatial resolution images of vine fields througout the vine growing season, UAVs could provide useful information, different than those normally considered in the literature. This study aimed at identifying i) relevant information that can be observed from UAV images by two kind of stakeholders : growers and advisers (G&A) ii) the most suitable periods for this observation iii) and the added value this information can have for both G&A daily tasks. This approach has been conducted on an 11.3 ha commercial vineyard representative of the south of France vineyards. UAV-based visible images (2.5 cm resolution) have been acquired in commercial conditions every two weeks from budburst to harvest. Images have been presented to two groups of G&A six times during the growing season. Every expert gathering was conducted with i) an individual period where images were presented to each expert ii) a collective period where G&As were invited to share information. Application of this methodology demonstrated that much information about the vines, the soil and the vineyard environment can be extracted from UAV-based visible images without any image processing. Results showed that this information have added value all along the growing cycle of the vine, particularly for advisers.

Type
UAV applications
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baluja, J, Diago, MP, Balda, P, Zorer, R, Meggio, F, Morales, F and Tardaguila, J 2012. Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrigation Science 30 (6), 511522.CrossRefGoogle Scholar
Burgos, S, Mota, M, Noll, D and Cannelle, B 2015. Use of very high-resolution airborne images to analyse 3D canopy architecture of a vineyard. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 40, 399403.CrossRefGoogle Scholar
Comba, L, Gay, P, Primicerio, J and Aimonino, DR 2015. Vineyard detection from unmanned aerial systems images. Computers and Electronics in Agriculture 114, 7887.Google Scholar
Coombe, BG 1995. Adoption of a system for identifying grapevine growth stages. Australian Journal of Grape and Wine Research 1, 100110.Google Scholar
Di Gennaro, S, Battiston, E, Di Marco, S, Facini, O, Matese, A, Nocentini, M, Palliotti, A and Mugnai, L 2016. Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex. Phytopathologia Mediterranea 55 (2), 262275.Google Scholar
Kasbari, M and Leroux, B 2016. Méthodologie pour l’usage d’un drone de catégorie E pour la détection de la flavescence dorée. Cahier des techniques de l’INRA, Mesure et métrologie 1 (2), 3035.Google Scholar
Matese, A, Primicerio, J, Di Gennaro, F, Fiorillo, E, Vaccari, FP and Genesio, L 2013. Development and application of an autonomous and flexible unmanned aerial vehicle for precision viticulture. Acta Horticulturae 978, 6369.Google Scholar
Mathews, A and Jensen, 2013. Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud. Remote Sensing 5 (5), 21642183.CrossRefGoogle Scholar
Zarco-Tejada, PJ, Guillén-Climent, ML, Hernández-Clemente, R, Catalina, A, González, MR and Martín, P 2013. Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agricultural and forest meteorology 171, 281294.CrossRefGoogle Scholar