Classification of the microstructural elements of the vegetal tissue of the pumpkin (Cucurbita pepo l.) using convolutional neural networks
De la Torre, Miguel
Avila George, Himer
Seguí Gil, Lucía
Mayor López, Luis
MetadataShow full item record
ABSTRACT Althoughknowledgeofthemicrostructureoffoodofvegetaloriginhelpsustounderstand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques—Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)— when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classiﬁer to discriminate between tissue structures. For the comparison, a database of microstructural elements images was obtained from pumpkin (Cucurbitapepo L.) micrographs. Two classiﬁers were implemented using CNN and RBNN, and statistical performance metrics were computed using a 5-fold cross-validation scheme. This process was repeated one hundred times with a random selection of images in each repetition. ThecomparisonshowedthattheclassiﬁersbasedonCNNproducedabetterﬁt,obtaining F1–score average of 89.42% in front of 83.83% for RBNN. In this study, the performance of classiﬁers based on CNN was signiﬁcantly higher compared to those based on RBNN in the discrimination of microstructural elements of vegetable foods.Mostrar más
Oblitas, J. ...[et al]. (2021). Classification of the microstructural elements of the vegetal tissue of the pumpkin (Cucurbita pepo l.) using convolutional neural networks. Applied Sciences (Switzerland), 11(4). https://doi.org/10.3390/app11041581
The following license files are associated with this item:
Showing items related by title, author, creator and subject.
Effect of different combinations of size and shape parameters in the percentage error of classification of structural elements in vegetal tissue of the pumpkin Cucurbita pepo L. using probabilistic neural networks Oblitas Cruz, Jimy; Castro Silupu, Wilson; Mayor Lopez, Luis (Universidad Privada del Norte, 2016)Acceso abiertoAbstract It was proceeded to determine the optimal combination of size and shape parameters in order to obtain the classification of structural elements with the lowest percentage of error. To this effect it was ...
Enhancing carrot convective drying by combining ethanol and ultrasound as pre-treatments: Effect on product structure, quality, energy consumption, drying and rehydration kinetics Costa Santos, Karoline; Souza Guedes, Jaqueline; Lindsay Rojas, Meliza; Reis Carvalho, Gisandro; Duarte Augusto, Pedro Esteves (Elsevier, 2020-05-08)Acceso abiertoABSTRACT Ultrasound was combined with ethanol to improve different aspects of carrot convective drying, evaluating both processing and product quality. The ultrasound in water treatment resulted in cellular swelling and ...
Implementación del mantenimiento predictivo para los equipos críticos del proceso de secado en una empresa papelera Mata García, Edinson Rodolfo (Universidad Privada del Norte, 2016-06-02)Acceso cerradoRESUMEN La implementación de un plan de mantenimiento predictivo mediante la técnica de análisis vibraciones de una planta papelera, tiene como objetivo diagnosticar el estado técnico de los equipos críticos, para presentar ...