Machine and Deep Learning in the Evaluation of Selected Qualitative Characteristics of Sweet Potatoes Obtained under Different Convective Drying Conditions

This paper discusses the use of various methods to distinguish between slices of sweet potato dried in different conditions. The drying conditions varied in terms of temperature, the values were: 60 degrees C, 70 degrees C, 80 degrees C, and 90 degrees C. Examination methods included instrumental texture analysis using a texturometer and digital texture analysis based on macroscopic images. Classification of acquired data involved the use of machine learning techniques using various types of artificial neural networks, such as convolutional neural networks (CNNs) and multi-layer perceptron (MLP). As a result, in the convective drying, changes in color darkening were found in products with the following temperature values: 60 degrees C (L = 83.41), 70 degrees C (L = 81.11), 80 degrees C (L = 79.02), and 90 degrees C (L = 75.53). The best-generated model achieved an overall classification efficiency of 77%. Sweet potato dried at 90 degrees C proved to be completely distinguishable from other classes, among which classification efficiency varied between 61-83% depending on the class. This means that image analysis using deep convolutional artificial neural networks is a valuable tool in the context of assessing the quality of convective-dried sweet potato slices.

Automated summary

This paper discusses the use of various methods to distinguish between slices of sweet potato dried in different conditions. The study found that image analysis using deep convolutional artificial neural networks is a valuable tool in assessing the quality of convective-dried sweet potato slices.