Fast Detection of Olive Trees Affected by Xylella Fastidiosa from UAVs Using Multispectral Imaging

Abstract

Xylella fastidiosa (Xf) is a well-known bacterial plant pathogen mainly transmitted by vector insects and is associated with serious diseases affecting a wide variety of plants, both wild and cultivated; it is known that over 350 plant species are prone to Xf attack. In olive trees, it causes olive quick decline syndrome (OQDS), which is currently a serious threat to the survival of hundreds of thousands of olive trees in the south of Italy and in other countries in the European Union. Controls and countermeasures are in place to limit the further spreading of the bacterium, but it is a tough war to fight mainly due to the invasiveness of the actions that can be taken against it. The most effective weapons against the spread of Xf infection in olive trees are the detection of its presence as early as possible and attacks to the development of its vector insects. In this paper, image processing of high-resolution visible and multispectral images acquired by a purposely equipped multirotor unmanned aerial vehicle (UAV) is proposed for fast detection of Xf symptoms in olive trees. Acquired images were processed using a new segmentation algorithm to recognize trees which were subsequently classified using linear discriminant analysis. Preliminary experimental results obtained by flying over olive groves in selected sites in the south of Italy are presented, demonstrating a mean Sørensen–Dice similarity coefficient of about 70% for segmentation, and 98% sensitivity and 93% precision for the classification of affected trees. The high similarity coefficient indicated that the segmentation algorithm was successful at isolating the regions of interest containing trees, while the high sensitivity and precision showed that OQDS can be detected with a low relative number of both false positives and false negatives.

Size/Age Models for Monitoring of the Pink Sea Fan Eunicella verrucosa (Cnidaria: Alcyonacea) and a Case Study Application

“…This could lead to visual methods for identification, quantification, and eventually, measurement of the colonies. For instance, visible, multispectral, or hyperspectral imaging could be used, as already has been carried out for other coral species [52][53][54][55], as well as in other fields of research, from the study of animals and plants [56][57][58] to marine biogeochemistry [59].…”

Unmanned Aerial Vehicles in Agriculture: A Survey

“…Xylella fastidiosa, which is a bacterial plant pathogen affecting a wide variety of plants (over 350), has been detected in olive trees by using high-resolution visible and multispectral images acquired by a Italdron 4HSE EVO, a medium size multirotor endowed with a 3D gimbal, a compact five band multispectral camera (MicaSense RedEdge-M), a high-resolution thermal camera (FLIR Vue Pro 640), and a high-resolution visible camera (Sony α7r) during flights with a height of tens of meters [47].…”

Bulk Drag Predictions of Riparian Arundo donax Stands through UAV-Acquired Multispectral Images

“…The digital processing of multispectral images acquired through UAV devices represents a useful tool to remotely observe the growth in riparian vegetation cover and, therefore, the hydrodynamic behavior of vegetated open channels colonized by riparian stands in natural phenological conditions. The first outcome of this research is represented by the direct correlation between NDVI derived by UAV-acquired multispectral images (NDVI UAV ) and LAI* measurements of the examined riparian vegetation cover, obtained by adopting the same methodology reported in previous precision agriculture studies on UAV-based crop production indicators [33][34][35]. Riparian vegetation NDVI UAV maps are extremely useful for flooding risk management in natural and urban vegetated areas.…”