@Article{rs17142465,
AUTHOR = {Strati, Virginia and Albéri, Matteo and Barbagli, Alessio and Boncompagni, Stefano and Casoli, Luca and Chiarelli, Enrico and Colla, Ruggero and Colonna, Tommaso and Elek, Nedime Irem and Galli, Gabriele and Gallorini, Fabio and Guastaldi, Enrico and Hasnain, Ghulam and Lopane, Nicola and Maino, Andrea and Mantovani, Fabio and Mantovani, Filippo and Mazzoli, Gian Lorenzo and Migliorini, Federica and Petrone, Dario and Pierini, Silvio and Raptis, Kassandra Giulia Cristina and Tiso, Rocchina},
TITLE = {Advancing Grapevine Disease Detection Through Airborne Imaging: A Pilot Study in Emilia-Romagna (Italy)},
JOURNAL = {Remote Sensing},
VOLUME = {17},
YEAR = {2025},
NUMBER = {14},
ARTICLE-NUMBER = {2465},
URL = {https://www.mdpi.com/2072-4292/17/14/2465},
ISSN = {2072-4292},
ABSTRACT = {Innovative applications of high-resolution airborne imaging are explored for detecting grapevine diseases. Driven by the motivation to enhance early disease detection, the method’s effectiveness lies in its capacity to identify isolated cases of grapevine yellows (Flavescence dorée and Bois Noir) and trunk disease (Esca complex), crucial for preventing the disease from spreading to unaffected areas. Conducted over a 17 ha vineyard in the Forlì municipality in Emilia-Romagna (Italy), the aerial survey utilized a photogrammetric camera capturing centimeter-level resolution images of the whole area in 17 minutes. These images were then processed through an automated analysis leveraging RGB-based spectral indices (Green–Red Vegetation Index—GRVI, Green–Blue Vegetation Index—GBVI, and Blue–Red Vegetation Index—BRVI). The analysis scanned the 1.24 · 109 pixels of the orthomosaic, detecting 0.4% of the vineyard area showing evidence of disease. The instances, density, and incidence maps provide insights into symptoms’ spatial distribution and facilitate precise interventions. High specificity (0.96) and good sensitivity (0.56) emerged from the ground field observation campaign. Statistical analysis revealed a significant edge effect in symptom distribution, with higher disease occurrence near vineyard borders. This pattern, confirmed by spatial autocorrelation and non-parametric tests, likely reflects increased vector activity and environmental stress at the vineyard margins. The presented pilot study not only provides a reliable detection tool for grapevine diseases but also lays the groundwork for an early warning system that, if extended to larger areas, could offer a valuable system to guide on-the-ground monitoring and facilitate strategic decision-making by the authorities.},
DOI = {10.3390/rs17142465}
}