Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

Abstract

Although knowledge of the microstructure of food of vegetal origin helps us to understand 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 (Cucurbita pepo 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. The comparison showed that the classifiers based on CNN produced a better fit, 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.

Study of the microstructure-property-processing relationship in five potato (Solanum tuberosum) varieties during the frying process based on an automatic classification system using convolutional neural networks

“…For this analysis, a multiple comparison was performed using Tukey's test with a simultaneous confidence level of 95%. The values obtained for the seven microstructural parameters are in the ranges found in other studies such as Calabaza (Mayor et al, 2011;Oblitas et al, 2021;Oblitas-Cruz et al, 2016).…”

Flesh of pumpkin from ecological farming as part of fruit suitable for non-destructive cultivar classification using computer vision

“…For example, Wittstruck et al [14] used UAV (Unmanned Aerial Vehicle)-Based RGB imagery for the detection of single pumpkin fruit in the field, counting fruits and the prediction of fruit size and weight. Oblitas-Cruz et al [15] and Oblitas et al [16] applied image processing combined with neural networks for the discrimination of microstructural elements of the pumpkin tissue. Image analysis was also used for the research related to the drying of pumpkin.…”