Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
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 JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Light Acquisition02:16

Light Acquisition

8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K

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

Squeeze-Excitation Attention-Guided 3D Inception ResNet for Aflatoxin B1 Classification in Almonds Using Hyperspectral Imaging.

Toxins·2026
Same author

Plasmid prevalence is independent of antibiotic resistance in environmental <i>Enterobacteriaceae</i>.

Microbial genomics·2025
Same author

Correlation awareness evolutionary sparse hybrid spectral band selection algorithm to detect aflatoxin B1 contaminated almonds using hyperspectral images.

Food chemistry·2025
Same author

Ferrocenyl β-Diketonate Compounds: Extended Ring Systems for Improved Anticancer Activity.

Chembiochem : a European journal of chemical biology·2024
Same author

Wastewater-based surveillance of SARS-CoV-2: Short-term projection (forecasting), smoothing and outlier identification using Bayesian smoothing.

The Science of the total environment·2024
Same author

A comprehensive, open-source data model for wastewater-based epidemiology.

Water science and technology : a journal of the International Association on Water Pollution Research·2024

相关实验视频

Updated: May 10, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K

基于深度学习的杏仁中阿弗拉托克素B1污染的检测,使用高光谱成像:专注于优化3D发射-恢复网络模型.

Md Ahasan Kabir1,2, Ivan Lee1, Sang-Heon Lee1

  • 1UniSA STEM, University of South Australia, Mawson Lakes, Adelaide, SA 5095, Australia.

Toxins
|April 25, 2025
PubMed
概括
此摘要是机器生成的。

检测杏仁中阿弗拉托克辛B1的污染对于食品安全至关重要. 使用高光谱成像的新型深度学习模型为识别受污染的杏仁提供了快速,准确和高效的方法.

关键词:
它们的AUC AUC.开始 ResNet 开始这就是ResNet ResNet.非洲毒素B1是什么?卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.功能选择 功能选择超光谱成像技术的使用.

更多相关视频

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.5K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.2K

相关实验视频

Last Updated: May 10, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.5K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.2K

科学领域:

  • 食品科学 食品科学 食品科学
  • 分析化学 分析化学
  • 计算机科学 计算机科学

背景情况:

  • 阿弗拉托克素B1是一种有毒的致癌物质,存在于杏仁等食品中,对健康构成重大风险.
  • 快速和非破坏性检测方法对于确保食品安全和防止阿弗拉托克辛B1污染的传播至关重要.

研究的目的:

  • 开发和评估一种新的深度学习方法,以使用超光谱成像来准确地分类被阿弗拉托克素B1污染的杏仁.
  • 将拟议的深度学习模型与传统的机器学习方法进行对比,以检测亚毒素B1的性能.

主要方法:

  • 使用了3D Inception-ResNet深度学习架构,为分类任务进行了微调.
  • 采用高光谱成像来捕获杏仁的光谱数据.
  • 实现了一个特征选择算法,以优化处理效率和减少光谱维度.

主要成果:

  • 拟议的轻量级3D Inception-ResNet模型实现了90.81%的验证准确性,F1得分为0.899,AUC为0.964.
  • 这种深度学习模型在分类被阿弗拉托克辛B1污染的杏仁方面显著优于SVM,RF,QDA和DT等传统方法.
  • 计算效率高的轻量级3D Inception-ResNet模型适用于实时工业应用.

结论:

  • 超光谱成像与深度学习相结合,提供了一种非常准确的方法来检测杏仁中的阿弗拉托克辛B1.
  • 开发的深度学习方法支持创建实时自动选系统,以提高食品安全.
  • 这项研究有助于降低食品供应链中与阿弗拉托克辛B1污染相关的健康风险.