Monitor and classify dough based on color image with deep learning

文献类型： 外文期刊

第一作者： Murengami, Bryan Gilbert

作者： Murengami, Bryan Gilbert;Jing, Xudong;Jiang, Hanhui;Liu, Xiaojuan;Mao, Wulan;Wang, Shaojin;Li, Rui;Fu, Longsheng;Fu, Longsheng;Fu, Longsheng;Mao, Wulan;Li, Yuedan;Chen, Xueyong

作者机构：

关键词： Fermented dough; Dough monitoring; Optimal bread production; Ensemble learning

期刊名称：JOURNAL OF FOOD ENGINEERING （ 影响因子：5.8； 五年影响因子：5.9 ）

ISSN： 0260-8774

年卷期： 2025 年 386 卷

页码：

收录情况： SCI

摘要： To address subjective, time-consuming, and labor-intensive manual methods for monitoring and classifying fermenting dough, an automated and non-destructive method is proposed. Deep learning model YOLOv8s and extracted features including dough surface area, contrast, and homogeneity from RGB color images were employed to monitor fermenting dough. The features were input to a stacked ensemble model (SEM) with base models SVM, AdaBoost, KNN, and RF, with AdaBoost as meta-learner to classify fermenting dough into under-fermented, fermented, and over-fermented. SEM demonstrated a high dough classification rate of 83%, with specific rates of 75% for under-fermented, 71% for fermented, and 90% for over-fermented dough. Results reviewed that combining dough surface area and texture features is effective for monitoring dough, and can be used in adjusting chamber conditions. Furthermore, SEM showed great ability in classifying fermenting dough. The proposed method offers a promising solution for improved bread quality and consistency in bread-making.

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