Proceedings Article | 20 November 2024
KEYWORDS: Spectroscopy, Vegetation, Statistical analysis, Time metrology, Remote sensing, Portability, Data acquisition, Calibration, Spectral response
In 2013, the Apulia region in Southern Italy experienced the first outbreak of Xylella fastidiosa (Xf), a highly dangerous plant pathogenic bacterium that infects olive trees. The impact of Xf on Apulian agriculture has been profound and multifaceted. Olive orchards have suffered severe damage, with the emergence of Olive Quick Decline Syndrome (OQDS) leading to widespread tree mortality. Beyond the economic losses faced by farmers, the olive trees’ cultural and environmental significance adds another layer of concern. For these reasons, different studies have been conducted to identify olive cultivars that are more resistant to the bacterium, such as “Leccino” and “FS-17”. In recent years, a regional plan has been established to support the replanting of felled olive trees in areas interested in past eradications, allowing to plant only two olive tree varieties “Leccino” and “FS-17”, tolerant to the bacterium. For this reason, it could be particularly interesting to use proximal and remote sensing techniques to detect and classify different olive tree cultivars. This work aims to evaluate the capabilities of vis-NIR spectroscopy/hyperspectral data analysis in the classification of three olive cultivars (“Leccino”, “Cima di Bitonto”, and “FS-17”). A portable spectroradiometer (FieldSpec4 Pro), equipped with a leaf clip, was used in the field for leaf spectral measurements. 108 leaf spectral signatures were acquired at different times (0, 2 and 24 hours) after the leaf detachment phase and analyzed to evaluate the variation in overall reflectance and in 65 vegetative indices value (NDVI, EVI, …) over time. The results obtained not only demonstrate the efficacy of spectral signature in cultivar classification, but also provide important information to optimize ground measurement campaigns and to set up the hyperspectral data collection, on wide areas, acquired by sensors installed on UAV, airborne or satellite.
