El frijol común es uno de los cultivos más consumidos en todo el mundo, y de gran importancia para la seguridad alimentaria en América Central. El objetivo de este estudio fue determinar la velocidad a la cual el frijol Matambú (Phaseolus vulgaris) se expande después del remojo del grano. Se desarrollo un sistema de procesamiento de imágenes digitales automatizado para medir el volumen de grano individual y reteniendo su integridad. La expansión volumétrica se evaluó con modelos Peleg y sigmoides para temperaturas de agua de 20-70 oC. Las ecuaciones de los parámetros cinéticos se describieron como funciones de temperatura utilizando la ecuación de Arrhenius con una energía de activación de 22.410 kJ mol-1. Los resultados obtenidos son potencialmente útiles para futuros estudios sobre propiedades de alimentos, procesos y diseño de envases.
There is a plethora of apps available for different biological applications, however, mobile software for plant measurement, specifically for field conditions are limited. Additionally, there is a need to create large training datasets for machine learning applications. A free app called Plantlia, which is available to download from the Google Play Store, aims to resolve this with an intuitive interface, and a method to automatically scale images using homography. Plantlia also includes methods to share results from either direct measurements or thresholded images. This paper aims to describe some of the functions of Plantlia, as well as show scenarios that display its performance. Images from cellphones and drone pictures were used for validation on different devices. This is important, as it means that users with low-cost equipment, like drones with no GPS information, can still analyze localized field information. Similarly, researchers can do communal efforts to share and receive in-field data to create machine learning datasets.
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