Hyperspectral Reflectance as a Basis to Discriminate Olive Varieties—A Tool for Sustainable Crop Management

Abstract

Worldwide sustainable development is threatened by current agricultural land change trends, particularly by the increasing rural farmland abandonment and agricultural intensification phenomena. In Mediterranean countries, these processes are affecting especially traditional olive groves with enormous socio-economic costs to rural areas, endangering environmental sustainability and biodiversity. Traditional olive groves abandonment and intensification are clearly related to the reduction of olive oil production income, leading to reduced economic viability. Most promising strategies to boost traditional groves competitiveness—such as olive oil differentiation through adoption of protected denomination of origin labels and development of value-added olive products—rely on knowledge of the olive varieties and its specific properties that confer their uniqueness and authenticity. Given the lack of information about olive varieties on traditional groves, a feasible and inexpensive method of variety identification is required. We analyzed leaf spectral information of ten Portuguese olive varieties with a powerful data-mining approach in order to verify the ability of satellite’s hyperspectral sensors to provide an accurate olive variety identification. Our results show that these olive varieties are distinguishable by leaf reflectance information and suggest that even satellite open-source data could be used to map them. Additional advantages of olive varieties mapping were further discussed.

High-Resolution UAV Imagery for Field Olive (Olea europaea L.) Phenotyping

“…Sola-Guirado et al [51], in an experiment carried out in Greece, developed a multiple linear regression based on NDVI, canopy volume and the average slope of the tree location, yielding an R 2 of 0.6. Although good performance of vegetative indices in discriminating olive cultivars and in yield forecasting was found in our study and in previous ones [49][50][51][52], it is important to pay attention to possible limitations in practical transferring of these techniques. For instance, the ability of vegetative indices in cultivar identification has been tested in experimental fields characterized by homogeneous pedo-climatic conditions and agronomic practices within the orchard [49, this experiment].…”

“…The olive cultivars evaluated in this study were different for canopy spectral response. While morphological markers are widely used for olive cultivars identification [46][47][48], little information is reported about leaf and canopy reflectance [49,50]. The combined use of leaf hyperspectral reflectance and data-mining techniques was successfully applied to discriminate 10 olive cultivars in an experiment carried out in Portugal [50].…”

“…While morphological markers are widely used for olive cultivars identification [46][47][48], little information is reported about leaf and canopy reflectance [49,50]. The combined use of leaf hyperspectral reflectance and data-mining techniques was successfully applied to discriminate 10 olive cultivars in an experiment carried out in Portugal [50]. In another experiment, Avola et al [49] used a UAV equipped with a multispectral camera (520-600, 630-690 and 760-900 bands) to discriminate field-grown olive cultivars (Frantoio and Leccino) in an experimental plantation with different scion/rootstock combinations.…”

In-field hyperspectral imaging dataset of Manzanilla and Gordal olive varieties throughout the season

“… The data published here can be used to compare acquisition methodologies, as a source for algorithms, or even to complement techniques with an in-field approach. A large number of remote sensing datasets are provided by governmental agencies, although the variety in field data is limited [9] , [10] , [11] . High-value development may be addressed to analyse olive properties or characteristics, study the correlation between olive signature variations during the growing season, compare different cultivars, or even analyse the variations with weather conditions.…”