An Agave Counting Methodology Based on Mathematical Morphology and Images Acquired through Unmanned Aerial Vehicles

Blue agave is an important commercial crop in Mexico, and it is the main source of the traditional mexican beverage known as tequila. The variety of blue agave crop known as Tequilana Weber is a crucial element for tequila agribusiness and the agricultural economy in Mexico. The number of agave plants in the field is one of the main parameters for estimating production of tequila. In this manuscript, we describe a mathematical morphology-based algorithm that addresses the agave automatic counting task. The proposed methodology was applied to a set of real images collected using an Unmanned Aerial Vehicle equipped with a digital Red-Green-Blue (RGB) camera. The number of plants automatically identified in the collected images was compared to the number of plants counted by hand. Accuracy of the proposed algorithm depended on the size heterogeneity of plants in the field and illumination. Accuracy ranged from 0.8309 to 0.9806, and performance of the proposed algorithm was satisfactory.This research was supported by the Spanish Ministerio de Economía y Competitividad, contract TIN2015-64395-R (MINECO/FEDER, UE), as well as by the Basque Government, contract IT900-16. This work was also supported in part by CONACYT (Mexico), grant 258033

Segmentación lingüística del color mediante un modelo bayesiano jerárquico

En este trabajo se realiza la fusión de técnicas Bayesianas de interacción espacial con un modelo de categorización del color para dar lugar a la segmentación lingüística. El modelo de categorización del color está basado en las 11 categorías universales de color propuestas por Berlin y Kay en 1969