Search Results (3)

Olive oil production is among the most significant pillars of crop production, especially in the Mediterranean region. The management risks undertaken throughout the olive oil production chain can be minimized using smart tools and applications. This review addressed the influence of the fruit fly of Bactrocera oleae (B. oleae) or Dacus oleae on the quality and antioxidant activity of the olives and their products based on the most recent literature data. Furthermore, in this review, we focused on the latest research achievements in remote sensor systems, features, and monitoring algorithms applied to remotely monitor plant diseases and pests, which are summarized here. Thus, this paper illustrates how precision agriculture technologies can be used to help agricultural decision-makers and to monitor problems associated with integrated pest management for crops and livestock, achieving agricultural sustainability. Moreover, challenges and potential future perspectives for the widespread adoption of these innovative technologies are discussed. Full article

Olive fruit fly Dacus oleae is a well-known pest infecting the bark of olive fruit, leading to reduction of extracted olive oil properties. Among chemicals proposed for Dacus oleae population control, pheromone 1,7-dioxaspiro(5.5)undecane (DSU), Dacus pheromone, is considered as a promising agent, which is added in several traps. However, all proposed systems manage to sufficiently deliver DSU for only two weeks. Furthermore, an additional problem is the limited available amount of pheromone to use in such systems. To overcome this, in the present study, a novel synthetic procedure of DSU is described, including only five steps. Intermediate products were studied by High Resolution Mass Spectroscopy Electrospray Ionization (HRMS-ESI) (m/z), while the resulting DSU was further characterized by ¹H and ¹³C-NMR. Synthesized DSU was further encapsulated in poly(L-lactic acid) (PLLA) microparticles in three different concentrations; 5, 10 and 20% w/w. Its successful incorporation was studied by FT-IR, XRD and differential scanning calorimeter (DSC) while two procedures, liquid extraction and solid phase microextraction, followed by GC-MS analysis, was used for quantification of pheromone to microparticles. It was found that microparticles loading was over 85% for all three formulations. Its release showed a prolonged profile for microparticles containing 20% w/w DSU, lasting four weeks, while the quantity of DSU released reached 100%. These microparticles could be appropriate to control Dacus oleae population. Full article

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. Full article