Version 1
: Received: 10 September 2022 / Approved: 14 September 2022 / Online: 14 September 2022 (04:04:05 CEST)
How to cite:
Hsia, C.; Lee, Y.; Lai, C. An Explainable and Lightweight Deep Convolutional Neural Network for Quality Detection of Green Coffee Beans. Preprints2022, 2022090190. https://doi.org/10.20944/preprints202209.0190.v1
Hsia, C.; Lee, Y.; Lai, C. An Explainable and Lightweight Deep Convolutional Neural Network for Quality Detection of Green Coffee Beans. Preprints 2022, 2022090190. https://doi.org/10.20944/preprints202209.0190.v1
Hsia, C.; Lee, Y.; Lai, C. An Explainable and Lightweight Deep Convolutional Neural Network for Quality Detection of Green Coffee Beans. Preprints2022, 2022090190. https://doi.org/10.20944/preprints202209.0190.v1
APA Style
Hsia, C., Lee, Y., & Lai, C. (2022). An Explainable and Lightweight Deep Convolutional Neural Network for Quality Detection of Green Coffee Beans. Preprints. https://doi.org/10.20944/preprints202209.0190.v1
Chicago/Turabian Style
Hsia, C., Yi-Hsuan Lee and Chin-Feng Lai. 2022 "An Explainable and Lightweight Deep Convolutional Neural Network for Quality Detection of Green Coffee Beans" Preprints. https://doi.org/10.20944/preprints202209.0190.v1
Abstract
In recent years, the demand for coffee has increased tremendously. During the production process, green coffee beans are traditionally screened manually for defective beans before they are packed into coffee bean packages; however, this method is not only time-consuming but also increases the rate of human error due to fatigue. Therefore, this paper proposed a lightweight deep convolutional neural network (LDCNN) for the quality detection system of green coffee beans, which combined depthwise separable convolution (DSC), squeeze-and-excite block (SE block), skip block, and other frameworks. To avoid the influence of low parameters of the lightweight model caused by the model training process, rectified Adam (RA), lookahead (LA), and gradient centralization (GC) were included to improve efficiency; the model was also put into the embedded system. Finally, the local interpretable model-agnostic explanations (LIME) model was employed to explain the predictions of the model. The experimental results indicated that the accuracy rate of the model could reach up to 98.38% and the F1 score could be as high as 98.24% when detecting the quality of green coffee beans. Hence, it can obtain higher accuracy, lower computing time, and lower parameters. Moreover, the interpretable model verified that the lightweight model in this work is reliable, providing the basis for screening personnel to understand the judgment through its interpretability, thereby improving the classification and prediction of the model.
Keywords
green coffee bean; lightweight framework; deep convolutional neural network; explainable model; random optimization
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
