RESUMEN
Olive oil is a key ingredient in the Mediterranean diet and offers many health benefits. However, many factors affect the quality and quantity of olive oil such as olive tree diseases and olive-related pests. Unfortunately, the procedure of identifying pests or the outbreak of a disease is time-consuming, and it depends heavily on the size of the olive grove. Through the use of ICT, remote monitoring of the olive grove can be achieved, by collecting environment-related data and having an overview of the olive grove's overall health. In this paper we propose a low-cost dense network of sensors that collects daily data regarding the olive grove, thus, providing the possibility to prevent infestation of olive fruit fly and/or the outbreak of olive tree-related disease.
Asunto(s)
Aceite de Oliva , Preparaciones Farmacéuticas , Tecnología de Sensores Remotos , Dieta Mediterránea , Frutas/química , Olea/química , Aceite de Oliva/química , Aceite de Oliva/aislamiento & purificación , Enfermedades de las Plantas/prevención & control , Aceites de Plantas/aislamiento & purificación , Tecnología de Sensores Remotos/tendenciasRESUMEN
Traditionally, the main process for olive fruit fly population monitoring is trap measurements. Although the above procedure is time-consuming, it gives important information about when there is an outbreak of the population and how the insect is spatially distributed in the olive grove. Most studies in the literature are based on the combination of trap and environmental data measurements. Strictly speaking, the dynamics of olive fruit fly population is a complex system affected by a variety of factors. However, the collection of environmental data is costly, and sensor data often require additional processing and cleaning. In order to study the volatility of correlation in trap counts and how it is connected with population outbreaks, a stochastic algorithm, based on a stochastic differential model, is experimentally applied. The results allow us to predict early population outbreaks allowing for more efficient and targeted spraying.
Asunto(s)
Agricultura , Algoritmos , Modelos Biológicos , Olea , Enfermedades de las Plantas , Tephritidae , Agricultura/métodos , Animales , Frutas/parasitología , Olea/parasitología , Enfermedades de las Plantas/parasitología , Enfermedades de las Plantas/prevención & control , Enfermedades de las Plantas/estadística & datos numéricos , Procesos Estocásticos , Tephritidae/fisiologíaRESUMEN
In the Gutenberg-Richter relation that describes the frequency-magnitude distribution of earthquakes, the b value represents the distribution's slope. Since b values can be used for mapping the dynamic response of earthquake source, methodologies for calculating robust b values are of great importance. Although nowadays software which is meant for statistical analysis of earthquake data can determine b values with high accuracy, in occasions where catalogs that contain small number of earthquake events, the produced results are not satisfactory. In this paper we present a new self-optimized algorithm for a more efficient calculation of the b value. The algorithm's results are compared with two widely known software for statistical analysis of earthquake data, showing a better performance in evaluating b values for earthquake catalogs containing small number of events.