Assessment of Vineyard Canopy Characteristics from Vigour Maps Obtained Using UAV and Satellite Imagery

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 7; p. 2363
• Main Authors: Campos, Javier, García-Ruíz, Francisco, Gil, Emilio
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.03.2021; MDPI
• Subjects: Accuracy; Aircraft; Automation; Canopies; Crops; Data points; Fruits; Methods; nanosatellite; Parameter estimation; pesticide application; Pesticides; Plantations; Population; Remote sensing; satellite; Satellite imagery; Satellites; unmanned aerial vehicle; Unmanned aerial vehicles; variable rate application; Vegetation growth; vineyard; Vineyards; Wineries & vineyards; Spain; vineyard; pesticide application; unmanned aerial vehicle; satellite; variable rate application; nanosatellite
• DOI: 10.3390/s21072363
• ISSN: 1424-8220

Canopy characterisation is a key factor for the success and efficiency of the pesticide application process in vineyards. Canopy measurements to determine the optimal volume rate are currently conducted manually, which is time-consuming and limits the adoption of precise methods for volume rate selection. Therefore, automated methods for canopy characterisation must be established using a rapid and reliable technology capable of providing precise information about crop structure. This research providedregression models for obtaining canopy characteristics of vineyards from unmanned aerial vehicle (UAV) and satellite images collected in three significant growth stages. Between 2018 and 2019, a total of 1400 vines were characterised manually and remotely using a UAV and a satellite-based technology. The information collected from the sampled vines was analysed by two different procedures. First, a linear relationship between the manual and remote sensing data was investigated considering every single vine as a data point. Second, the vines were clustered based on three vigour levels in the parcel, and regression models were fitted to the average values of the ground-based and remote sensing-estimated canopy parameters. Remote sensing could detect the changes in canopy characteristics associated with vegetation growth. The combination of normalised differential vegetation index (NDVI) and projected area extracted from the UAV images is correlated with the tree row volume (TRV) when raw point data were used. This relationship was improved and extended to canopy height, width, leaf wall area, and TRV when the data were clustered. Similarly, satellite-based NDVI yielded moderate coefficients of determination for canopy width with raw point data, and for canopy width, height, and TRV when the vines were clustered according to the vigour. The proposed approach should facilitate the estimation of canopy characteristics in each area of a field using a cost-effective, simple, and reliable technology, allowing variable rate application in vineyards.