Automatic measurement of acidity from roasted coffee beans images using efficient deep learning

Abstract

Sourness is one of the basic yet essential tastes of coffee that is chemically composed of acids and quantitatively represented in the pH scale. Current tools for measuring the acidity level in roasted coffee beans, including traditional methods, require brewing sample coffee and probing the chemical components, limiting the applicability to end customers seeking to estimate the acidity level before choosing the right coffee beans to purchase. This paper proposes a novel approach to directly estimate the acidity levels from roasted coffee beans images by framing the problem into an image classification task, where a picture of roasted coffee beans is categorized into its appropriate pH range. As a result, end customers could simply estimate coffee beans' acidity levels by taking photos with conventional cameras. Multiple traditional machine learning and deep learning algorithms are validated for their ability to predict the correct acidity levels. The experiment results reveal that EfficientNet yields the best performance with an average F1 of 0.71 when trained with images from separate portable devices. Practical Applications The research's findings could also be extended to applications in the coffee‐industrial settings, such as automatically monitoring roasted coffee beans' quality from image and video streams. For end customers, the trade‐off between efficacy and efficiency of the EfficientNet algorithm is also investigated, which sheds light on the implementation aspects of state‐of‐the‐art deep learning models in portable devices such as smartphones or cameras. Such applications could prove to be a cost‐effective and convenient solution for customers to quickly measure roasted coffee beans' sourness before deciding to purchase.

Determination of shrinkage characteristics of pumpkin discs during convection, microwave, and combined drying using computer‐aided technique: Image processing application

“…Image processing is now used in a variety of fields, including medicine and biology, geography, archeological remains detection, physics (spectrometers, electron microscope images), space sciences (satellites), industrial applications (process and production supervision), consumer electronics, and food production processes. Studies on the application of image processing in the food industry, such as determining the amount of adulteration, estimating color, and classifying vegetables and fruits, determining morphological changes such as shrinkage after drying are on the rise (Chang et al, 2022; Erbakan et al, 2021; Fermo et al, 2021; Gonzatti et al, 2022; Hu et al, 2016; Jagtap et al, 2019; Kheiralipour & Pormah, 2017; Pedreschi et al, 2011; Ropelewska & Szwejda‐Grzybowska, 2021; Sajjacholapunt, 2022; Solak & Altinişik, 2018). In a research conducted by Yadollahinia et al, it was used an image processing system that could measure the continued shrinkage of potato slices during drying with high precision in short intervals to investigate the effect of temperature on shrinkage and moisture changes during convective potato slice drying.…”