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

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.

A feature extraction unsupervised neural network for an environmental data set.

Environmental data sets are characterized by a huge amount of heterogeneous data from external fields. As the number of measured points grows, a strategy is needed to select and efficiently analyze the useful information from the whole data set. One efficient way of obtaining the validation-compression of data sets is the adoption of a restricted set of features that describe, with an assigned accuracy a subset of the whole data set. One characteristic feature of the environmental data is time dependency: in the medium and long term they are not stationary data sets. The aim of this work is to propose a feature extraction technique based on a new model of an unsupervised neural network suitable to analyze this kind of data. The paper reports the results obtained utilizing the above extraction and analysis procedure on a real data set on chemical pollutants. It is shown that the proposed neural network is able to identify correctly human and/or meteorological effects in the environmental data set.