DIEA-Artículos
URI permanente para esta colecciónhttps://hdl.handle.net/10953/234
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Examinando DIEA-Artículos por Autor "Cano Marchal, Pablo"
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Ítem Automatic Detection of Olive Tree Canopies for Groves with Thick Plant Cover on the Ground(MDPI, 2022-08-19) Illana Rico, Sergio; Martínez Gila, Diego Manuel; Cano Marchal, Pablo; Gómez Ortega, JuanMarking the tree canopies is an unavoidable step in any study working with high-resolution aerial images taken by a UAV in any fruit tree crop, such as olive trees, as the extraction of pixel features from these canopies is the first step to build the models whose predictions are compared with the ground truth obtained by measurements made with other types of sensors. Marking these canopies manually is an arduous and tedious process that is replaced by automatic methods that rarely work well for groves with a thick plant cover on the ground. This paper develops a standard method for the detection of olive tree canopies from high-resolution aerial images taken by a multispectral camera, regardless of the plant cover density between canopies. The method is based on the relative spatial information between canopies.The planting pattern used by the grower is computed and extrapolated using Delaunay triangulation in order to fuse this knowledge with that previously obtained from spectral information. It is shown that the minimisation of a certain function provides an optimal fit of the parameters that define the marking of the trees, yielding promising results of 77.5% recall and 70.9% precision.Ítem Automatic System for the Detection of Defects on Olive Fruits in an Oil Mill(MDPI, 2021-09-03) Cano Marchal, Pablo; Satorres Martínez, Silvia; Gómez Ortega, Juan; Gámez García, JavierThe ripeness and sanitary state of olive fruits are key factors in the final quality of the virgin olive oil (VOO) obtained. Since even a small number of damaged fruits may significantly impact the final quality of the produced VOO, the olive inspection in the oil mill reception area or in the first stages of the productive process is of great interest. This paper proposes and validates an automatic defect detection system that utilizes infrared images, acquired under regular operating conditions of an olive oil mill, for the detection of defects on individual fruits. First, the image processing algorithm extracts the fruits based on the iterative application of the active contour technique assisted with mathematical morphology operations. Second, the defect detection is performed on the segmented olives using a decision tree based on region descriptors. The final assessment of the algorithm suggests that it works effectively with a high detection rate, which makes it suitable for the VOO industry.Ítem Expert system based on computer vision to estimate the content of impurities in olive oil samples(ELSEVIER, 2013-06-01) Cano Marchal, Pablo; Martínez Gila, Diego Manuel; Gámez García, Javier; Gómez Ortega, JuanThe determination of the content of impurities is a very frequent analysis performed on virgin olive oil samples, but the official method is quite work-intensive, and it would be convenient to have an alternative approximate method to evaluate the performance of the impurity removal process. In this work we develop a system based on computer vision and pattern recognition to classify the content of impurities of the olive oil samples in three sets, indicative of the goodness of the separation process of olive oil after its extraction from the paste. Starting from the histograms of the channels of the Red–Green–Blue (RGB), CIELAB and Hue-Saturation-Value (HSV) color spaces, we construct an initial input parameter vector and perform a feature extraction previous to the classification. Several linear and non-linear feature extraction techniques were evaluated, and the classifiers used were Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs). The best classification rate achieved was 87.66%, obtained using Kernel Principal Components Analysis (KPCA) and a grade-3-polynomial kernel SVM. The best result using ANNs was 82.38%, yielded by the use of Principal Component Analysis (PCA) with the Perceptron.Ítem On-line system based on hyperspectral information to estimate acidity, moisture and peroxides in olive oil samples(ELSEVIER, 2015-06-18) Martínez Gila, Diego Manuel; Cano Marchal, Pablo; Gámez García, Javier; Gómez Ortega, JuanThe analysis of the quality of virgin olive oil involves the determination of a series of properties, such as chemical indexes and organoleptic characteristics. In addition, the determination of these properties in real-time could be useful in order to improve the olive oil extraction process since the process parameters could be regulated with the real-time moisture information. In this paper, the feasibility of using a non-invasive hyperspectral device, in order to determine on-line three parameters of the olive oil (free acidity, peroxide index and moisture) is studied. In order to study the correlation between these parameters and the information obtained by the hyperspectral sensor (absorption level), three different methods were applied: genetic algorithms (GA), least absolute shrinkage and selection operator (LASSO), and successive projection algorithm (SPA). From the experimental results, reduced values in cross validation were obtained and the optimal wavelengths were pointed out.Ítem Optimal Production Planning for the Virgin Olive Oil Elaboration Process(ELSEVIER, 2016-04-25) Cano Marchal, Pablo; Martínez Gila, Diego Manuel; Gámez García, Javier; Gómez Ortega, JuanThe quality and obtained quantity of Virgin Olive Oil is bounded by the characteristics of the olives to be processed, and further determined by the influence of the process variables during the actual elaboration. Since the quality of the olives evolves during the harvesting season, it is relevant to consider when to harvest the olives in order to maximize the profit over the whole season. This work proposes a method to determine an optimal production plan for the whole harvesting season and presents the results obtained in its application to four different scenarios.Ítem Prediction of Fruity Aroma Intensity and Defect Presence in Virgin Olive Oil Using an Electronic Nose(MDPI, 2021-03-25) Cano Marchal, Pablo; Sanmartin, Chiara; Satorres Martínez, Silvia; Gómez Ortega, Juan; Mencarelli, Fabio; Gámez García, JavierThe organoleptic profile of a Virgin Olive Oil is a key quality parameter that is currently obtained by human sensory panels. The development of an instrumental technique capable of providing information about this profile quickly and online is of great interest. This work employed a general purpose e-nose, in lab conditions, to predict the level of fruity aroma and the presence of defects in Virgin Olive Oils. The raw data provided by the e-nose were used to extract a set of features that fed a regressor to predict the level of fruity aroma and a classifier to detect the presence of defects. The results obtained were a mean validation error of 0.5 units for the prediction of fruity aroma using lasso regression; and 88% accuracy for the defect detection using logistic regression. Finally, the identification of two out of ten specific sensors of the e-nose that can provide successful results paves the way to the design of low-cost specific electronic noses for this application.Ítem Production Planning for Agroalimentary Laboratories Using Customer Satisfaction Criteria(IEEE, 2021-11-08) Garzón Casado, Álvaro; Cano Marchal, Pablo; Gómez Ortega, Juan; Gámez García, JavierAgroalimentary laboratories typically process samples that require different types of analysis depending on the substance being analyzed and the requirements of the clients. A key parameter for client satisfaction is the time that it takes since the samples arrive at the laboratory facilities and the results are provided to the client. Thus, the order in which the different samples are processed and the analysis are performed can have a significant impact in the overall customer satisfaction. This paper proposes a novel approach for planning the production of agroalimentary laboratories based on maximizing a measure of customer satisfaction derived from this lag between sample reception and analysis finalization. This way, a planning model is defined which, based on the basic version of the Resource-Constrained Project Scheduling Problem, can be used to optimize an objective function that focuses on the client satisfaction, considering the relative relevance of clients and samples. The paper includes a detailed presentation of the parameters, variables and constraints required to define the problem, along with the corresponding modeling assumptions. The applicability of the approach is presented with a discussion of the solutions provided by the optimization problem for a set of scenarios of interest, which show the suitability of the method as a systematic tool for planning the operations of agroalimentary laboratories.Ítem Stochastic season-wide optimal production planning of virgin olive oil(ELSEVIER, 2018-11-06) Cano Marchal, Pablo; Martínez Gila, Diego Manuel; Gámez García, Javier; Gómez Ortega, JuanThe quality and obtained quantity of Virgin Olive Oil is determined by the influence of the process variables during the production process based on the properties of the olives being processed. Since the quality of the olives evolve during the harvesting season, a relevant question is to systematically suggest when to harvest the olives in order to maximize the profit over the whole season. This work proposes a method to determine an optimal production plan for the whole harvesting season explicitly considering the stochastic nature of the problem, the uncertainty in the problem parameters and the information available at each step, and presents the results obtained in its application to different production scenarios.Ítem Stochastic season-wide optimal production planning of virgin olive oil(Elsevier, 2018-12) Cano Marchal, Pablo; Martínez Gila, Diego Manuel; Gámez García, Javier; Gómez Ortega, JuanThe quality and obtained quantity of Virgin Olive Oil is determined by the influence of the process variables during the production process based on the properties of the olives being processed. Since the quality of the olives evolve during the harvesting season, a relevant question is to systematically suggest when to harvest the olives in order to maximize the profit over the whole season. This work proposes a method to determine an optimal production plan for the whole harvesting season explicitly considering the stochastic nature of the problem, the uncertainty in the problem parameters and the information available at each step, and presents the results obtained in its application to different production scenarios.Ítem Visualization and Interpretation Tool for Expert Systems Based on Fuzzy Cognitive Maps(IEEE, 2019) Garzón Casado, Álvaro; Cano Marchal, Pablo; Gómez Ortega, Juan; Gámez García, JavierThis paper presents a python library that includes a toolkit with the aim of improving the interpretability of expert systems based on fuzzy cognitive maps through improvements in the visualization and representation of the graphs that can be drawn using the variables of the resulting models. The motivation for the development of the library arises from the need to improve the interpretability of the aforementioned expert systems, given that their multilayer and extracted from experts’ knowledge nature can make them very difficult to interpret even for the expert user. Throughout the paper, the reader will be introduced to the basic features of fuzzy logic and fuzzy cognitive maps, and the different developed tools will be defined and exemplified.Ítem Zero Defect Manufacturing in the Food Industry: Virgin Olive Oil Production(MDPI, 2022-05-20) Satorres Martínez, Silvia; Rico Illana, Sergio; Cano Marchal, Pablo; Martínez Gila, Diego; Gómez Ortega, JuanThis paper provides a zero defect manufacturing (ZDM) approach designed for the virgin olive oil (VOO) industry, with the objective of producing the best possible product using sustainable methods. A deep analysis of related work for ZDM and the current state-of-the-art technology in the VOO elaboration process is presented, along with the implications of the well-known trade-off between quality and extraction yield and the importance of having the right information on the state of the fruits and the main technological variables of the process. Currently available new technologies, such as smart devices with cloud connectivity, enable having the required amount of data and information in real-time, thus making the concept of ZDM possible. Together with the proposed ZDM approach and strategies, the basic requirements and the first steps towards the implementation of ZDM in this productive sector are identified.