Abstract: In this work a study for the role of different environmental factors to the evolution of olive fruit fly, via an appropriate network of population traps is given. More explicitly, the olive fruit fly is a parasitic insect that infests olive groves in many countries. Through the use of a network of traps a simulation model was developed and used to simulate the dispersion of olive fruit fly inside a real olive grove for different environmental factors, such as different starting areas of olive fruit fly presence, different temperature sets as well as different drifting distances. Results showed that the level of infestation of the grove was not dependent on the limited areas the olive fruit fly emerged but on the drifting distance a fly could travel per day.
Abstract: Olive fruit fly trap measurements are used as one of the indicators for ol-ive grove infestation, and therefore, as a consultation tool on spraying parameters. In this paper, machine learning techniques are used to predict the next olive fruit fly trap measurement, given as input environmental parameters and knowledge of previous trap measurements. Various classification algorithms are employed and applied to different environmental settings, in extensive comparative experiments, in order to detect the impact of the latter on olive fruit fly population prediction.
Abstract: The paper introduces a new probabilistic algorithm for spatial interpolation of sampling data. The algorithm lies on new finding in literature concerning scaling behaviour of complex systems. It is demonstrated that there is a critical spatial scale over which statistical behaviour of local subsystems coincides with the statistical behaviour of the entire system. This observation in combination with local spatial statistical trends is used in order to construct a more efficient algorithm for interpolation of spatial sampling data. Application of the proposed algorithm to real population data of olive fruit fly is demonstrated. Simulation results and evaluation procedures in order to test the robustness of the proposed algorithm are also presented.
Abstract: Lately lot of work has been done in the area of modeling insect population dynamics based on the emergency of new information technologies. In this paper we propose a real-time online alert information system for estimating the population evolution of the olive fruit fly. The proposed system simulates the evolution of the biological cycle of olive fruit fly as well as its activity in real field using mobile platforms (smartphones, tablets) for the distributed collection of data from traps and local climate and topological data thus predicting timely olive fruit fly outbreaks. Simulation results are depicted validating the robustness of the proposed information system and revealing the importance of appropriate population control measures in the right time and place.
Abstract: The aim of the present work is to bring together new tools and developments in physics and computer science with new aspects in applied entomology. Our work elaborates on well known studies on applied entomology in population insect dynamics. A spatial evolution equation for olive fly population is proposed in order to describe more accurately outbreaks of insect populations by incorporating random movement or dispersion of the population. It turns out that dispersion causes both acceleration of population growth and shift of the high stable population equilibrium to even higher values thus producing population outbreak. Simulation results are also presented confirming theoretically predicted behavior of outbreaks in earlier times.
Abstract: The role of fruit bearing percentage in olive fruit fly infestation is investigated through a simulation model where the spatial law of dispersion distances were modeled via an appropriate exponential law. The dispersal of olive fruit flies was simulated for two distinct cases, an olive grove with no olive fruits and an olive grove with 100% olive fruit bearing. Results showed that when no olive fruits were present the olive fruit flies scatter in all directions away of the starting point, while when the olive grove is full of olive fruits the olive fruit flies form a cluster around the starting position with almost zero mean travel distance.
Abstract: Για την αντιμετώπιση του σημαντικότερου εχθρού της ελιάς, του δάκου (Bactrocera oleae Gmelin), απαραίτητη προϋπόθεση αποτελεί ο καθορισμός των πληροφοριών που σχετίζονται με βιοτικούς και αβιοτικούς παράγοντες του ελαιώνα, η συμπεριφορά των οποίων καθορίζεται από ένα σύνολο τυχαίων αλληλεπιδράσεων. Η μελέτη της εξέλιξης του πληθυσμού των εντόμων με την βοήθεια μαθηματικών μοντέλων αποτελεί διεθνώς ένα ισχυρό εργαλείο στην προσπάθεια κατανόησης του φαινομένου της έξαρσης του πληθυσμού τους. Σκοπός της παρούσας μελέτης είναι ο συνυπολογισμός αυτών των τυχαίων αλληλεπιδράσεων σε προϋπάρχοντα μαθηματικά μοντέλα (στοχαστικές διαφορικές εξισώσεις), όπως αυτές υπεισέρχονται στις τιμές των παραμέτρων των μοντέλων, σχετικά με την εξέλιξη του πληθυσμού του δάκου.