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

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4915 ◽  
Author(s):  
Attilio Di Nisio ◽  
Francesco Adamo ◽  
Giuseppe Acciani ◽  
Filippo Attivissimo
Keyword(s):  
Multispectral Imaging ◽  
Xylella Fastidiosa ◽  
Similarity Coefficient ◽  
Segmentation Algorithm ◽  
Dice Similarity Coefficient ◽  
The European Union ◽  
The South ◽  
Linear Discriminant ◽  
Fast Detection ◽  
Olive Trees

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.

scholarly journals An Evaluation of Monitoring Surveys of the Quarantine Bacterium Xylella Fastidiosa Performed in Containment and Buffer Areas of Apulia, Southern Italy

2019 ◽  
Vol 24 (2) ◽  
pp. 96-99 ◽  
Author(s):  
Marco Scortichini ◽  
Gianluigi Cesari
Keyword(s):  
Pathogen Detection ◽  
Southern Italy ◽  
Plant Protection ◽  
Xylella Fastidiosa ◽  
Epidemiological Data ◽  
Molecular Techniques ◽  
Tree Age ◽  
The European Union ◽  
Detection Techniques ◽  
Olive Trees

Introduction: Xylella fastidiosa is a quarantine phytopathogen for the European Plant Protection Organization and currently infects olive trees in the Apulia region (southern Italy). Upon the Implementing Decision of the European Union 2016/764 of May 12, 2016, extensive monitoring surveys were performed on approximately 190 000 ha to ascertain the possible occurrence of X. fastidiosa. Objectives: The primary objectives of the analysis were to start to collect epidemiological data on X. fastidiosa occurrence in areas far from the initial outbreaks and discuss the results of the pathogen detection. Methods: A total of 220 279 olive trees were inspected. Basic information on farm and trees management was obtained. A total of 13 706 olive trees were analyzed through serological and molecular techniques to verify the possible occurrence of the bacterium. Results: The cultivars “Nociara,” “Cima di Melfi,” and “Cellina di Nardò” showed the highest occurrence of decline symptoms. Tree age appears to be related to the incidence of decline symptoms. Olive trees growing in well-managed soils showed fewer symptoms than trees cultivated in farms where such agronomic techniques are not regularly performed. X. fastidiosa was detected in 2078 samples taken from symptomatic trees and 1653 samples obtained from asymptomatic trees. In 3300 samples taken from symptomatic trees, the bacterium was not detected. Conclusions: Implementation and utilization of reliable in situ detection techniques could increase the number of sampled trees in each plot, thus allowing a more extensive and robust assessment of X. fastidiosa–infected plants in areas where the pathogen inoculums are still low.

scholarly journals Genome-Wide Analysis Provides Evidence on the Genetic Relatedness of the Emergent Xylella fastidiosa Genotype in Italy to Isolates from Central America

2017 ◽  
Vol 107 (7) ◽  
pp. 816-827 ◽  
Author(s):  
Annalisa Giampetruzzi ◽  
Maria Saponari ◽  
Giuliana Loconsole ◽  
Donato Boscia ◽  
Vito Nicola Savino ◽  
...  
Keyword(s):  
Costa Rica ◽  
Genetic Relatedness ◽  
Xylella Fastidiosa ◽  
Conjugative Plasmid ◽  
Nucleotide Polymorphisms ◽  
The South ◽  
Gene Characterization ◽  
Pan Genome ◽  
Atpase Gene ◽  
Olive Trees

Xylella fastidiosa is a plant-pathogenic bacterium recently introduced in Europe that is causing decline in olive trees in the South of Italy. Genetic studies have consistently shown that the bacterial genotype recovered from infected olive trees belongs to the sequence type ST53 within subspecies pauca. This genotype, ST53, has also been reported to occur in Costa Rica. The ancestry of ST53 was recently clarified, showing it contains alleles that are monophyletic with those of subsp. pauca in South America. To more robustly determine the phylogenetic placement of ST53 within X. fastidiosa, we performed a comparative analysis based on single nucleotide polymorphisms (SNPs) and the study of the pan-genome of the 27 currently public available whole genome sequences of X. fastidiosa. The resulting maximum-parsimony and maximum likelihood trees constructed using the SNPs and the pan-genome analysis are consistent with previously described X. fastidiosa taxonomy, distinguishing the subsp. fastidiosa, multiplex, pauca, sandyi, and morus. Within the subsp. pauca, the Italian and three Costa Rican isolates, all belonging to ST53, formed a compact phylotype in a clade divergent from the South American pauca isolates, also distinct from the recently described coffee isolate CFBP8072 imported into Europe from Ecuador. These findings were also supported by the gene characterization of a conjugative plasmid shared by all the four ST53 isolates. Furthermore, isolates of the ST53 clade possess an exclusive locus encoding a putative ATP-binding protein belonging to the family of histidine kinase-like ATPase gene, which is not present in isolates from the subspecies multiplex, sandyi, and pauca, but was detected in ST21 isolates of the subspecies fastidiosa from Costa Rica. The clustering and distinctiveness of the ST53 isolates supports the hypothesis of their common origin, and the limited genetic diversity among these isolates suggests this is an emerging clade within subsp. pauca.

scholarly journals Semi-Automatic Method for Early Detection of Xylella fastidiosa in Olive Trees Using UAV Multispectral Imagery and Geostatistical-Discriminant Analysis

2020 ◽  
Vol 13 (1) ◽  
pp. 14
Author(s):  
Annamaria Castrignanò ◽  
Antonella Belmonte ◽  
Ilaria Antelmi ◽  
Ruggiero Quarto ◽  
Francesco Quarto ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Early Detection ◽  
Plant Pathogens ◽  
Early Stage ◽  
Xylella Fastidiosa ◽  
Mediterranean Countries ◽  
Parametric Classification ◽  
Aerial Vehicle ◽  
Olive Groves ◽  
Olive Trees

Xylella fastidiosa subsp. pauca (Xfp) is one of the most dangerous plant pathogens in the world. Identified in 2013 in olive trees in south–eastern Italy, it is spreading to the Mediterranean countries. The bacterium is transmitted by insects that feed on sap, and causes rapid wilting in olive trees. The paper explores the use of Unmanned Aerial Vehicle (UAV) in combination with a multispectral radiometer for early detection of infection. The study was carried out in three olive groves in the Apulia region (Italy) and involved four drone flights from 2017 to 2019. To classify Xfp severity level in olive trees at an early stage, a combined method of geostatistics and discriminant analysis was implemented. The results of cross-validation for the non-parametric classification method were of overall accuracy = 0.69, mean error rate = 0.31, and for the early detection class of accuracy 0.77 and misclassification probability 0.23. The results are promising and encourage the application of UAV technology for the early detection of Xfp infection.

scholarly journals HPLC-MS/MS method applied to an untargeted metabolomics approach for the diagnosis of “olive quick decline syndrome”

2021 ◽  
Author(s):  
Sabrina Di Masi ◽  
Giuseppe E. De Benedetto ◽  
Cosimino Malitesta ◽  
Maria Saponari ◽  
Cinzia Citti ◽  
...  
Keyword(s):  
Bacterial Infections ◽  
High Performance ◽  
High Resolution Mass Spectrometry ◽  
Xylella Fastidiosa ◽  
Long Chain Fatty Acids ◽  
Powerful Approach ◽  
Diffusible Signal Factor ◽  
Profile Changes ◽  
Olive Quick Decline Syndrome ◽  
Olive Trees

AbstractOlive quick decline syndrome (OQDS) is a disorder associated with bacterial infections caused by Xylella fastidiosa subsp. pauca ST53 in olive trees. Metabolic profile changes occurring in infected olive trees are still poorly investigated, but have the potential to unravel reliable biomarkers to be exploited for early diagnosis of infections. In this study, an untargeted metabolomic method using high-performance liquid chromatography coupled to quadrupole-time-of-flight high-resolution mass spectrometry (HPLC-ESI-Q-TOF-MS) was used to detect differences in samples (leaves) from healthy (Ctrl) and infected (Xf) olive trees. Both unsupervised and supervised data analysis clearly differentiated the groups. Different metabolites have been identified as potential specific biomarkers, and their characterization strongly suggests that metabolism of flavonoids and long-chain fatty acids is perturbed in Xf samples. In particular, a decrease in the defence capabilities of the host after Xf infection is proposed because of a significant dysregulation of some metabolites belonging to flavonoid family. Moreover, oleic acid is confirmed as a putative diffusible signal factor (DSF). This study provides new insights into the host-pathogen interactions and confirms LC-HRMS-based metabolomics as a powerful approach for disease-associated biomarkers discovery in plants. Graphical abstract

scholarly journals Controlling the Spatial Spread of a Xylella Epidemic

2021 ◽  
Vol 83 (4) ◽  
Author(s):  
Sebastian Aniţa ◽  
Vincenzo Capasso ◽  
Simone Scacchi
Keyword(s):  
Spatial Structure ◽  
Numerical Simulations ◽  
Control Strategies ◽  
Xylella Fastidiosa ◽  
Cost Effective ◽  
Olive Tree ◽  
Control Measures ◽  
Weed Biomass ◽  
Spatial Spread ◽  
Olive Trees

AbstractIn a recent paper by one of the authors and collaborators, motivated by the Olive Quick Decline Syndrome (OQDS) outbreak, which has been ongoing in Southern Italy since 2013, a simple epidemiological model describing this epidemic was presented. Beside the bacterium Xylella fastidiosa, the main players considered in the model are its insect vectors, Philaenus spumarius, and the host plants (olive trees and weeds) of the insects and of the bacterium. The model was based on a system of ordinary differential equations, the analysis of which provided interesting results about possible equilibria of the epidemic system and guidelines for its numerical simulations. Although the model presented there was mathematically rather simplified, its analysis has highlighted threshold parameters that could be the target of control strategies within an integrated pest management framework, not requiring the removal of the productive resource represented by the olive trees. Indeed, numerical simulations support the outcomes of the mathematical analysis, according to which the removal of a suitable amount of weed biomass (reservoir of Xylella fastidiosa) from olive orchards and surrounding areas resulted in the most efficient strategy to control the spread of the OQDS. In addition, as expected, the adoption of more resistant olive tree cultivars has been shown to be a good strategy, though less cost-effective, in controlling the pathogen. In this paper for a more realistic description and a clearer interpretation of the proposed control measures, a spatial structure of the epidemic system has been included, but, in order to keep mathematical technicalities to a minimum, only two players have been described in a dynamical way, trees and insects, while the weed biomass is taken to be a given quantity. The control measures have been introduced only on a subregion of the whole habitat, in order to contain costs of intervention. We show that such a practice can lead to the eradication of an epidemic outbreak. Numerical simulations confirm both the results of the previous paper and the theoretical results of the model with a spatial structure, though subject to regional control only.

scholarly journals Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 893
Author(s):  
Yazan Qiblawey ◽  
Anas Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Serkan Kiranyaz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Early Stage ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Lung Region ◽  
Computed Tomography Images ◽  
Feature Pyramid ◽  
Severity Levels

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

scholarly journals Further In Vitro Assessment and Mid-Term Evaluation of Control Strategy of Xylella fastidiosa subsp. pauca in Olive Groves of Salento (Apulia, Italy)

Pathogens ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 85
Author(s):  
Giuseppe Tatulli ◽  
Vanessa Modesti ◽  
Nicoletta Pucci ◽  
Valeria Scala ◽  
Alessia L’Aurora ◽  
...  
Keyword(s):  
Control Strategy ◽  
Xylella Fastidiosa ◽  
In Planta ◽  
Bacterial Concentration ◽  
Infected Area ◽  
Olive Groves ◽  
Term Evaluation ◽  
In Vitro Assessment ◽  
Olive Trees

During recent years; Xylella fastidiosa subsp. pauca (Xfp) has spread in Salento causing relevant damage to the olive groves. Measures to contain the spreading of the pathogen include the monitoring of the areas bordering the so-called “infected” zone and the tree eradication in case of positive detection. In order to provide a control strategy aimed to maintain the tree productivity in the infected areas, we further evaluated the in vitro and in planta mid-term effectiveness of a zinc-copper-citric acid biocomplex. The compound showed an in vitro bactericidal activity and inhibited the biofilm formation in representative strains of X. fastidiosa subspecies, including Xfp isolated in Apulia from olive trees. The field mid-term evaluation of the control strategy assessed by quantitative real-time PCR in 41 trees of two olive groves of the “infected” area revealed a low concentration of Xfp over the seasons upon the regular spraying of the biocomplex over 3 or 4 consecutive years. In particular, the bacterial concentration lowered in July and October with respect to March, after six consecutive treatments. The trend was not affected by the cultivar and it was similar either in the Xfp-sensitive cultivars Ogliarola salentina and Cellina di Nardò or in the Xfp-resistant Leccino. Moreover, the scoring of the number of wilted twigs over the seasons confirmed the trend. The efficacy of the treatment in the management of olive groves subjected to a high pathogen pressure is highlighted by the yielded a good oil production

scholarly journals Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning

2021 ◽  
pp. 002203452110053
Author(s):  
H. Wang ◽  
J. Minnema ◽  
K.J. Batenburg ◽  
T. Forouzanfar ◽  
F.J. Hu ◽  
...  
Keyword(s):  
Gold Standard ◽  
Orthodontic Treatment ◽  
3D Models ◽  
Similarity Coefficient ◽  
Patient Specific ◽  
Dice Similarity Coefficient ◽  
Surface Deviations ◽  
Surface Deviation ◽  
Binary Segmentation ◽  
Time Required

Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment planning. Although various (semi)automated methods have been proposed to segment the jaw or the teeth, there is still a lack of fully automated segmentation methods that can simultaneously segment both anatomic structures in CBCT scans (i.e., multiclass segmentation). In this study, we aimed to train and validate a mixed-scale dense (MS-D) convolutional neural network for multiclass segmentation of the jaw, the teeth, and the background in CBCT scans. Thirty CBCT scans were obtained from patients who had undergone orthodontic treatment. Gold standard segmentation labels were manually created by 4 dentists. As a benchmark, we also evaluated MS-D networks that segmented the jaw or the teeth (i.e., binary segmentation). All segmented CBCT scans were converted to virtual 3-dimensional (3D) models. The segmentation performance of all trained MS-D networks was assessed by the Dice similarity coefficient and surface deviation. The CBCT scans segmented by the MS-D network demonstrated a large overlap with the gold standard segmentations (Dice similarity coefficient: 0.934 ± 0.019, jaw; 0.945 ± 0.021, teeth). The MS-D network–based 3D models of the jaw and the teeth showed minor surface deviations when compared with the corresponding gold standard 3D models (0.390 ± 0.093 mm, jaw; 0.204 ± 0.061 mm, teeth). The MS-D network took approximately 25 s to segment 1 CBCT scan, whereas manual segmentation took about 5 h. This study showed that multiclass segmentation of jaw and teeth was accurate and its performance was comparable to binary segmentation. The MS-D network trained for multiclass segmentation would therefore make patient-specific orthodontic treatment more feasible by strongly reducing the time required to segment multiple anatomic structures in CBCT scans.

Matronianus, Comes Isauriae: an Inscription from an Early Byzantine Basilica at Yanıkhan, Rough Cilicia

1985 ◽  
Vol 35 ◽  
pp. 93-97 ◽  
Author(s):  
Stephen Hill
Keyword(s):  
The South ◽  
Late Professor ◽  
The Road ◽  
Scrub Oak ◽  
The North ◽  
Wild Olive ◽  
On The Road ◽  
Olive Trees ◽  
The Village ◽  
Early Byzantine

The ruins at Yanıkhan form the remains of a Late Roman village in the interior of Rough Cilicia some 8 kilometres inland from the village of Limonlu on the road to Canbazlı (see Fig. 1). The site has not been frequently visited by scholars, and the first certain reference to its existence was made by the late Professor Michael Gough after his visit on 2 September 1959. Yanıkhan is now occupied only by the Yürüks who for years have wintered on the southern slopes of Sandal Dağ. The ancient settlement at Yanıkhan consisted of a village covering several acres. The remains are still extensive, and some, especially the North Basilica, are very well preserved, but there has been considerable disturbance in recent years as stone and rubble have been removed in order to create small arable clearings. The visible remains include many domestic buildings constructed both from polygonal masonry without mortar and from mortar and rubble with coursed smallstone facing. There are several underground cisterns and a range of olive presses. The countryside around the settlement has been terraced for agricultural purposes in antiquity, and is, like the settlement itself, densely covered with scrub oak and wild olive trees. The most impressive remains are those of the two basilical churches which are of little artistic pretension, but considerable architectural interest. The inscription which forms the substance of this article was found on the lintel block of the main west entrance of the South Basilica.

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