Monitor and classify dough based on color image with deep learning

IF 5.3 2区 农林科学 Q1 ENGINEERING, CHEMICAL Journal of Food Engineering Pub Date : 2024-08-28 DOI:10.1016/j.jfoodeng.2024.112299
Bryan Gilbert Murengami , Xudong Jing , Hanhui Jiang , Xiaojuan Liu , Wulan Mao , Yuedan Li , Xueyong Chen , Shaojin Wang , Rui Li , Longsheng Fu
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Abstract

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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利用深度学习,根据彩色图像监控面团并对其进行分类
为了解决监测和分类发酵面团的主观、耗时和耗力的人工方法,提出了一种自动化和非破坏性的方法。利用深度学习模型 YOLOv8s 和从 RGB 彩色图像中提取的面团表面积、对比度和均匀性等特征来监测发酵面团。这些特征被输入到以 SVM、AdaBoost、KNN 和 RF 为基础模型,以 AdaBoost 为元学习器的堆叠集合模型(SEM)中,将发酵面团分为发酵不足、发酵和发酵过度。SEM 的面团分类率高达 83%,发酵不足的面团分类率为 75%,发酵的面团分类率为 71%,发酵过度的面团分类率为 90%。结果表明,结合面团表面积和质地特征可有效监测面团,并可用于调整腔室条件。此外,扫描电子显微镜在发酵面团的分类方面也显示出很强的能力。所提出的方法为提高面包质量和面包制作的一致性提供了一种很有前景的解决方案。
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来源期刊
Journal of Food Engineering
Journal of Food Engineering 工程技术-工程:化工
CiteScore
11.80
自引率
5.50%
发文量
275
审稿时长
24 days
期刊介绍: The journal publishes original research and review papers on any subject at the interface between food and engineering, particularly those of relevance to industry, including: Engineering properties of foods, food physics and physical chemistry; processing, measurement, control, packaging, storage and distribution; engineering aspects of the design and production of novel foods and of food service and catering; design and operation of food processes, plant and equipment; economics of food engineering, including the economics of alternative processes. Accounts of food engineering achievements are of particular value.
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