A Comparison of Fuzzy Clustering Algorithms Applied to Feature Extraction on Vineyard

Image segmentation is a process by which an image is partitioned into regions with similar features. Many approaches have been proposed for color image segmentation, but Fuzzy C-Means has been widely used, because it has a good performance in a large class of images. However, it is not adequate for noisy images and it also takes more time for execution as compared to other method as K-means. For this reason, several methods have been proposed to improve these weaknesses. Method like Possibilistic C-Means, Fuzzy Possibilistic C-Means, Robust Fuzzy Possibilistic C-Means and Fuzzy C-Means with Gustafson-Kessel algorithm. In this paper we perform a comparison of these clustering algorithms applied to feature extraction on vineyard images. Segmented images are evaluated using several quality parameters such as the rate of correctly classied area and runtim

Pixel classification through Mahalanobis distance for identification of grapevine canopy elements on RGB images

Vine vigour and fruit-cluster exposure to sunlight in a grapevine canopy fruiting zone has been shown to strongly correlate with key fruit composition and diseases incidence. In this framework, the use of automated image analysis for the identification of plant elements is an important issue to be addressed for vineyard assessment (Dunn and Martin, 2004). In addition, optimum segmentation method is strongly application dependent and thus needs to be tested for each particular case (Cheng et al., 2001). The objective of the present work is to propose and test a simple, rapid and practical method for the identification of two relevant elements of grapevines canopy: clusters and green leaves

Aplicación y evaluación de metodologías activas de enseñanza-aprendizaje que faciliten la adquisición de competencias relacionadas con los Sistemas de Información Geográfica en los programas de Grado y Máster de la Universidad de La Rioja

[ES] En las últimas décadas se ha generalizado la utilización de sistemas de información, gracias a una mayor disponibilidad de datos, así como a la mejora de la potencia y capacidad de los sistemas y programas informáticos. En concreto, el manejo de los datos geográficos mediante los denominados Sistemas de Información Geográficos (SIG) ha supuesto una revolución en la capacidad de obtención de información y avance de la ciencia. En este trabajo se han identificado las asignaturas de Grado y Máster de la Universidad de la Rioja en las que se aplican metodologías activas de enseñanza de los SIG. Se ha diseñado y realizado una encuesta de autoevaluación que nos ha permitido valorar el punto de partida de los conocimientos sobre SIG de los que disponen los estudiantes. Así mismo, en estas asignaturas se está utilizando el material didáctico creado para el aprendizaje y cuya estructura responde precisamente al nivel de uso y demanda de los SIG y datos geográficos en general y en particular de los universitarios. Con los resultados obtenidos se puede analizar la eficiencia de la metodología diseñada para el aprendizaje.[EN] In recent decades, the use of information systems has become widespread, thanks to greater data availability, as well as the improvement of the power and capacity of computer systems and programs. Specifically, the management of geographical data through the so-called Geographic Information Systems (GIS) has brought about a revolution in the ability to obtain information and advanced science. In this work, a list of the Undergraduate and Master's subjects of the University of La Rioja in which active GIS teaching methodologies are applied has been identified. A self-evaluation survey has been designed and carried out that has allowed us to assess the starting point of the knowledge about GIS that students have. Likewise, in these subjects a novel didactic material created for GIS learning is being used and its structure responds precisely to the level of use and demand of GIS and geographic data in general and in particular of university students. With the results obtained, the efficiency of the methodology designed for learning can be analyzedDiago Santamaria, MP.; Andrades Rodríguez, MS.; Aransay Azofra, JM.; Llorente Adán, JÁ.; Ruíz Flaño, P.; Lana-Renault Monreal, NS. (2021). Aplicación y evaluación de metodologías activas de enseñanza-aprendizaje que faciliten la adquisición de competencias relacionadas con los Sistemas de Información Geográfica en los programas de Grado y Máster de la Universidad de La Rioja. En IN-RED 2020: VI Congreso de Innovación Educativa y Docencia en Red. Editorial Universitat Politècnica de València. 239-249. https://doi.org/10.4995/INRED2020.2020.12008OCS23924

Feature Extraction on Vineyard by Gustafson Kessel FCM and K-means

Image segmentation is a process by which an image is partitioned into regions with similar features. Many approaches have been proposed for color images segmentation, but Fuzzy C-Means has been widely used, because it has a good performance in a wide class of images. However, it is not adequate for noisy images and it takes longer runtimes, as compared to other method like K-means. For this reason, several methods have been proposed to improve these weaknesses. Methods like Fuzzy C-Means with Gustafson-Kessel algorithm (FCM-GK), which improve its performance against the noise, but increase significantly the runtime. In this paper we propose to use the centroids generated by GK-FCM algorithms as seeding for K-means algorithm in order to accelerate the runtime and improve the performance of K-means with random seeding. These segmentation techniques were applied to feature extraction on vineyard images. Segmented images were evaluated using several quality parameters such as the rate of correctly classified area and runtime