ABSTRACT
Over the last 50 years, many approaches for extracting plant key parameters from remotely sensed data have been developed, especially in the last decade with the spread of unmanned aerial vehicles (UAVs) in agriculture. Multispectral sensors are very useful for the elaboration of common vegetation indices (VIs), however, the spectral accuracy and range may not be enough. In this scenario, hyperspectral (HS) technologies are gaining particular attention thanks to the highest spectral resolution, which allows deep characterization of vegetative/soil response. Literature presents few papers encompassing UAV-based HS applications in vineyard, a challenging conditions respect to other crops due to high presence of bare soil, grass cover, shadows and high heterogeneity canopy structure with different leaf inclination. The purpose of this paper is to present the first contribution combining traditional and multivariate HS data elaboration techniques, supported by strong ground truthing of vine ecophysiological, vegetative and productive variables. Firstly the research describes the UAV image acquisition and processing workflow to generate a 50 bands HS orthomosaic of a study vineyard. Subsequently, the spectral data extracted from 60 sample vines were elaborated both investigating the relationship between traditional narrowband VIs and grapevine traits. Then, multivariate calibration models were built using a double approach based on Partial Least Square (PLS) regression and interval-PLS (iPLS), to evaluate the correlation performance between the biophysical parameters and HS imagery using the whole spectral range and a selection of more relevant bands applying a variable selection algorithm, respectively. All techniques (VIs, PLS and iPLS) provided satisfactory correlation performances for the ecophysiological (R 2 = 0.65), productive (R 2 = 0.48), and qualitative (R 2 = 0.63) grape parameters. The novelty of this work is represented by the first assessment of a UAV HS dataset with the expression of the entire vine ecosystem, from the physiological and vegetative state to grapes production and quality, using narrowband VIs and multivariate PLS regressions. A correct non-destructive estimation of key parameters in vineyard, above all physiological parameters which must be measured in a short time as they are extremely influenced by the variability of environmental conditions during the day, represents a powerful tool to support the winegrower in vineyard management.
ABSTRACT
Two separate artificial sensors, an electronic eye (EE) and an electronic tongue (ET), were recently developed to monitor grape ripening based on the analysis of must. The aim of this research is to exploit the complementary information obtained by means of EE and ET sensing systems using different data fusion strategies, in order to develop an integrated device able to quickly and easily quantify the physico-chemical parameters that are used to assess phenolic ripeness. To this purpose, both low-level and mid-level data fusion approaches were investigated. Partial Least Squares (PLS) regression was applied to the fused data, with the aim of relating the information brought by the two sensors with twelve physico-chemical parameters measured on the must samples by standard analytical methods. The results achieved with mid-level data fusion outperformed those obtained using EE and ET separately, and highlighted that both the artificial sensors have made a significant contribution to the prediction of each one of the considered physico-chemical parameters.
