Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Plant Breeding and Biotechnology

Plant Breeding and Biotechnology

Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

Fructose-Induced bioenergetic surplus Unlocks fatty acid biosynthesis pathway dominance over reverse β-Oxidation: Mechanistic insights into High-Caproate production from food waste.

Bioresource technology·2026

Same author

Citrus sunki Peel Extract Enhances Proliferation and Differentiation of Fibro-Adipocyte Progenitors in Holstein Cattle for Cultivated Meat Production.

Food science of animal resources·2026

Same author

Effect of Opioid-Sparing Anesthesia on Postoperative Nausea and Vomiting After Breast Surgery: A Single-Center Randomized Controlled Trial.

Journal of clinical medicine·2026

Same author

Detection of depression risk among older adults using home-deployed socially assistive robots: a real-world study.

Frontiers in psychiatry·2026

Same author

Strategic physicochemical tuning: amphiphilic polymers as molecular regulators of amyloid aggregation and cytotoxicity.

Chemical science·2026

Same author

Risk Factors for Postoperative Hyperbilirubinemia in Living Donors Undergoing Living Donor Liver Transplantation.

Clinical transplantation·2026

Search research articles

相关实验视频

Updated: Jul 10, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

使用机器学习研究适应性面包机

Jooho Lee¹, Youngjin Kim¹, Sangoh Kim¹

¹Department of Plant and Food Engineering, Sangmyung University, Cheonan 31066, Republic of Korea.

Foods (Basel, Switzerland)

|November 25, 2023

概括

此摘要是机器生成的。

烤中的人工智能优化了面包的质量. 一个新的制过程预测模型 (BPPM) 使用机器学习和传感器数据来预测制阶段,与传统方法相比,导致面包体积更大.

关键词:

人工智能的人工智能是人工智能.烤过程预测预测面包制造商的面包制造商计算机视觉计算机视觉机器学习是机器学习.

更多相关视频

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

相关实验视频

Last Updated: Jul 10, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

科学领域:

食品科学与技术食品科学与技术
人工智能的人工智能
机器学习机器学习

背景情况:

面包是全球消费的一种主食.
现代食品加工需要先进的技术来进行质量控制和优化.
人工智能 (AI) 为实时监控和改善食品生产过程提供解决方案.

研究的目的:

开发和评估一个人工智能驱动的系统来监测和分析面包过程.
使用传感器和视觉数据自主预测烤阶段.
通过智能过程控制,提高面包质量和生产效率.

主要方法:

实现一个综合解决方案,将传感器数据和机器学习技术结合起来.
基于数据的机器学习模型的开发:过程预测模型 (BPPM).
实时监测和分析使用BPPM烤过程中的环境变量和影响.

主要成果:

在监测和分析实时数据方面,BPPM表现出色.
该模型自主预测烤阶段,改善面包质量.
使用BPPM生产的面包量明显大 (p <0.05) 比使用固定时间生产的面包量大.

结论:

开发的AI系统显示了提高食品生产行业的精度和效率的巨大潜力.
这种方法为食品行业内人工智能应用的未来进展奠定了基础.
BPPM成功地改善了面包质量和生产成果.