Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Key Elements for Plant Nutrition02:35

Key Elements for Plant Nutrition

18.8K
Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...
18.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Effects of Ascorbic Acid on Apoptosis, Metabolism, and Muscle Quality in Ammonia-Stressed Rainbow Trout (<i>Oncorhynchus mykiss</i>).

Foods (Basel, Switzerland)·2026
Same author

The role of oleic acid-induced aggregation and glycation modifications in peptide release from duck myofibrillar proteins.

Food chemistry·2026
Same author

In Silicon Deciphering Atomic-Scale Structural Units in Peptide Glass.

Angewandte Chemie (International ed. in English)·2026
Same author

BN-Embedded Dibenzoullazine: Synthesis, Structures, and Properties.

Organic letters·2026
Same author

Ratiometric Mycotoxin Detection in Living Plants With Dual-Emissive Nanosensors.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Investigation on the Fatigue and Rutting Behavior of Asphalt Binder Containing Compound Warm Mixing Agent.

Materials (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jul 5, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion.

Yuzhe Han1, Qimin Cheng1, Wenjin Wu2

  • 1School of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan 430074, China.

Foods (Basel, Switzerland)
|January 17, 2024
PubMed
Summary

This study introduces DPF-Nutrition, a novel method for estimating nutritional intake from single (monocular) images. By integrating depth prediction and RGB-D fusion, it significantly improves the accuracy of automated dietary health monitoring.

Keywords:
RGB-D fusiondeep learningdepth predictionnutrition estimation

More Related Videos

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
06:21

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Published on: February 19, 2021

5.7K
Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq
04:54

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

Published on: March 19, 2021

4.6K

Related Experiment Videos

Last Updated: Jul 5, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
06:21

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Published on: February 19, 2021

5.7K
Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq
04:54

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

Published on: March 19, 2021

4.6K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Nutrition Science

Background:

  • A balanced diet is crucial for overall health.
  • Automated nutrition estimation using deep learning and food images aids in dietary monitoring.
  • Monocular image-based methods offer convenience but suffer from accuracy limitations.

Purpose of the Study:

  • To develop an end-to-end nutrition estimation method using monocular images that overcomes accuracy limitations.
  • To enhance food portion estimation and overall nutrition assessment accuracy.

Main Methods:

  • Proposed DPF-Nutrition, an automated nutrition estimation method utilizing monocular food images.
  • Introduced a depth prediction module to generate depth maps for improved food portion estimation.
  • Designed an RGB-D fusion module to combine monocular images with predicted depth information.

Main Results:

  • The DPF-Nutrition method demonstrated improved performance in nutrition estimation.
  • Integration of depth prediction and RGB-D fusion techniques enhanced accuracy compared to existing methods.
  • Experiments on the Nutrition5k dataset validated the effectiveness and efficiency of the proposed approach.

Conclusions:

  • DPF-Nutrition represents a pioneering integration of depth prediction and RGB-D fusion for food nutrition estimation.
  • The method offers a more accurate and efficient solution for automated dietary health monitoring.
  • This approach holds significant potential for promoting healthier eating habits through precise nutritional tracking.