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Modern agriculture is facing unique challenges in building a sustainable future for food production, in which the reliable detection of plantation threats is of critical importance. The breadth of existing information sources, and their equivalent sensors, can provide a wealth of data which, to be useful, must be transformed into actionable knowledge. Approaches based on Information Communication Technologies (ICT) have been shown to be able to help farmers and related stakeholders make decisions on problems by examining large volumes of data while assessing multiple criteria. In this paper, we address the automated identification (and count the instances) of the major threat of olive trees and their fruit, the Bactrocera Oleae (a.k.a. Dacus) based on images of the commonly used McPhail trap’s contents. Accordingly, we introduce the “Dacus Image Recognition Toolkit” (DIRT), a collection of publicly available data, programming code samples and web-services focused at supporting research aiming at the management the Dacus as well as extensive experimentation on the capability of the proposed dataset in identifying Dacuses using Deep Learning methods. Experimental results indicated performance accuracy (mAP) of 91.52% in identifying Dacuses in trap images featuring various pests. Moreover, the results also indicated a trade-off between image attributes affecting detail, file size and complexity of approaches and mAP performance that can be selectively used to better tackle the needs of each usage scenario.

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The aim of our study was to investigate the prevalence of smoking in a Greek working population. A questionnaire regarding smoking habit was collected from 1,005 out of 1,200 blue and white-collar employees (response rate: 84%). The overall smoking prevalence was 48.4% and did not differ by sex, age, education, and occupation. The mean cigarette consumption per day was 25.54, with no difference observed by occupation. The above-mentioned findings, if confirmed by further research, are alarming and inconsistent with the prevalent pattern of smoking habits in the West.

A collection of 64 fig (Ficus carica L.) accessions was characterized through the use of RAPD markers, and results were evaluated in conjunction with morphological and agronomical characters, in order to determine the genetic relatedness of genotypes with diverse geographic origin. The results indicate that fig cultivars have a rather narrow genetic base. Nevertheless, RAPD markers could detect enough polymorphism to differentiate even closely related genotypes (i.e., clones of the same cultivar) and a unique fingerprint for each of the genotypes studied was obtained. No wasteful duplications were found in the collection. Cluster analysis allowed the identification of groups in accordance with geographic origin, phenotypic data and pedigree. Taking into account the limited information concerning fig cultivar development, the results of this study, which provide information on the genetic relationships of genetically distinct material, dramatically increase the fundamental and practical value of the collection and represent an invaluable tool for fig germplasm management.