Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Optimal Foraging

Optimal Foraging

How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.

Light Acquisition

Light Acquisition

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.

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point served as...

You might also read

Related Articles

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

Sort by

Same author

Hybrid Decoding with Co-Occurrence Awareness for Fine-Grained Food Image Segmentation.

Foods (Basel, Switzerland)·2026

Same author

IGSMNet: Ingredient-Guided Semantic Modeling Network for Food Nutrition Estimation.

Foods (Basel, Switzerland)·2025

Same author

Multi-View Edge Attention Network for Fine-Grained Food Image Segmentation.

Foods (Basel, Switzerland)·2025

Same author

Applications of Artificial Intelligence in Food Industry.

Foods (Basel, Switzerland)·2025

Same author

Recent advances in fish cutting: From cutting schemes to automatic technologies and internet of things innovations.

Comprehensive reviews in food science and food safety·2024

Same author

Deep Learning-Based Near-Infrared Hyperspectral Imaging for Food Nutrition Estimation.

Foods (Basel, Switzerland)·2023

Search research articles

Related Experiment Video

Updated: Jun 7, 2026

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

A Lightweight Hybrid Model with Location-Preserving ViT for Efficient Food Recognition.

Guorui Sheng¹, Weiqing Min^2,3, Xiangyi Zhu¹

¹School of Information and Electrical Engineering, Ludong University, Yantai 264025, China.

|January 23, 2024

Summary

A new Efficient Hybrid Food Recognition Net (EHFR-Net) improves mobile food image recognition by combining Convolutional Neural Networks (CNN) and Vision Transformers (ViT). This method enhances dietary management through accurate, lightweight AI on smartphones.

Keywords:

ViT food recognition global feature lightweight nutrition management

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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

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

Published on: February 9, 2024

Related Experiment Videos

Last Updated: Jun 7, 2026

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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

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

Published on: February 9, 2024

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Intelligent nutrition management relies on accurate food-image recognition.
Lightweight deep learning models are essential for mobile deployment in dietary tracking.
Existing Vision Transformers (ViT) excel at global information but neglect spatial details.

Purpose of the Study:

To develop a novel, lightweight neural network for efficient food-image recognition on mobile devices.
To address the limitations of ViT in preserving spatial information for food recognition.
To integrate local and global feature extraction for improved accuracy.

Main Methods:

Proposed an Efficient Hybrid Food Recognition Net (EHFR-Net) integrating CNNs and ViTs.
Introduced a Location-Preserving Vision Transformer (LP-ViT) to retain positional information.
Utilized inverted residual blocks for lightweight local feature extraction and a unified Hybrid Block (HBlock) for feature integration.

Main Results:

EHFR-Net achieved 90.7% accuracy on the ETHZ Food-101 dataset, outperforming MobileViTv2 by 3.5%.
The model demonstrates superior performance compared to state-of-the-art lightweight ViT-based networks.
Experiments confirmed the effectiveness of the proposed LP-ViT and HBlock integration.

Conclusions:

The EHFR-Net offers a highly accurate and efficient solution for mobile food-image recognition.
The developed LP-ViT effectively preserves spatial information, enhancing ViT performance in this domain.
This research advances intelligent nutrition management through improved AI-powered dietary tools.