Assessing Grapevine Biophysical Parameters From Unmanned Aerial Vehicles Hyperspectral Imagery

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 (R2 = 0.65), productive (R2 = 0.48), and qualitative (R2 = 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.

Vegetation Biomass and Nitrogen Content Estimation Using Ensembles: Regression and Active Learning Strategies for Field Sampling Reduction

“…The principal novelty introduced in this paper is the exploitation of ensembles of active learning and regression techniques for vegetation parameters estimation using a calibration set significantly reduced compared to previous literature studies. In this regard, the main focus was the optimization of estimation methodologies, mostly based on regression, for the improvement of estimates through active learning [3] and/or ad hoc selection of regression variables [7]. However, the problem of the amount of calibration data feeding such regressions has been underestimated.…”

“…It represents an incremental monitoring framework starting with the first flight over the study area. These data are used to extract processing variables that, as suggested in [3], [7], [12], are represented by calibrated reflectance bands and/or a collection of vegetation indices. Indeed, indications provided in literature about the set of variables to be used in this kind of applications are quite variegated, as they may depend on the scene, the type of crop and the parameters to be estimated.…”

“…PLSR is a consolidated linear statistical approach suitable for the analysis of multi-collinear spectral datasets, thus fully exploiting redundant information [22]. It represents the literature standard for crop biophysical parameters estimation from HS data [3], [7], [12]. In this phase, the optimum number of components is determined through a leave-one-out cross validation [27].…”

“…However, several studies have indicated that more detailed description of an area variability is beneficial for precision agriculture applications [6]. Therefore, precision agriculture is often implemented by means of cameras mounted on drones, due to the high spatial and spectral resolution this sensing configuration can ensure [7], [8], [9]. This means that, in most cases, the local scale is considered attractive by stakeholders and that the figure of one sample per hectare indicated in some literature [10] could be insufficient or insignificant for the problem at hand, if randomly selected.…”

Assessing the capacity of high-resolution commercial satellite imagery for grapevine downy mildew detection and surveillance in New York state

“…However, most prior work on grapevine remote sensing has aimed to detect water stress, with few studies addressing other abiotic parameters such as nitrogen content, yield, and fruit composition (Giovos et al 2021). Reports on proximal and remote sensing of grapevine diseases are dominated by near-surface platforms and increasingly by hyperspectral cameras (both proximal and airborne) (Naidu et al 2009; Oerke, Herzog, and Toepfer 2016; MacDonald et al 2016; Bendel et al 2020; Gao et al 2020; Lacotte et al 2022; Sawyer et al 2023; di Gennaro et al 2016; Matese et al 2022; Cséfalvay et al 2009; Galvan et al 2023). Most studies aim to detect disease at a single point in time, while season-long, operational surveillance systems remain largely unexplored.…”