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

You might also read

Related Articles

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

Sort by
Same author

FCA-STNet: Spatiotemporal Growth Prediction and Phenotype Extraction from Image Sequences for Cotton Seedlings.

Plants (Basel, Switzerland)·2025
Same author

A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs.

Plants (Basel, Switzerland)·2025
Same author

Perinatal outcomes following cell-free DNA screening in >32 000 women: Clinical follow-up data from a single tertiary center.

Prenatal diagnosis·2018
Same author

Research on Thermal Conductivity of Electrospun Polyacrilonitrile-Multi-Walled Carbon Nanotubes Composite Carbon Nanofiber Papers.

Journal of nanoscience and nanotechnology·2018
Same author

Investigation of potential toxic components based on the identification of Genkwa Flos chemical constituents and their metabolites by high-performance liquid chromatography coupled with a Q Exactive high-resolution benchtop quadrupole Orbitrap mass spectrometer.

Journal of separation science·2018
Same author

The caudal dorsal artery generates hematopoietic stem and progenitor cells via the endothelial-to-hematopoietic transition in zebrafish.

Journal of genetics and genomics = Yi chuan xue bao·2018

Related Experiment Video

Updated: May 31, 2025

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:47

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

Published on: February 14, 2018

11.2K

Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning.

Bo Han1,2,3, Jingjing Zhang1,2,3, Rolla Almodfer4

  • 1College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.

Foods (Basel, Switzerland)
|January 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an automated apple grading system using computer vision and machine learning, significantly improving efficiency and accuracy over manual methods. The system achieves high performance in stem detection and apple classification.

Keywords:
appleartificial intelligenceimage segmentationmachine learningmodel compressionquality gradingstem detectionstructural re-parameterization

More Related Videos

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.2K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K

Related Experiment Videos

Last Updated: May 31, 2025

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:47

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

Published on: February 14, 2018

11.2K
Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.2K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K

Area of Science:

  • Food Science
  • Computer Vision
  • Machine Learning

Background:

  • Manual apple grading is inefficient and subjective.
  • Automated systems are needed to improve grading accuracy and efficiency.

Purpose of the Study:

  • To develop an automated apple grading system using computer vision, image processing, and machine learning.
  • To reduce human interference and enhance grading efficiency and accuracy.

Main Methods:

  • Developed a lightweight detection algorithm (FDNet-p) for stem feature capture.
  • Proposed an improved DPC-AWKNN segmentation algorithm for apple body segmentation.
  • Utilized image processing for feature extraction (color, shape, diameter) and developed a GBDT-based grading model.

Main Results:

  • FDNet-p achieved 96.6% mAP@0.5 for stem detection with low computational cost (3.4 GFLOPs, 2.5 MB).
  • The GBDT grading model demonstrated superior performance with weighted Jacard Score, Precision, Recall, and F1 Score of 0.9506, 0.9196, 0.9683, and 0.9513, respectively.
  • The system offers innovative solutions for automated fruit grading detection and classification.

Conclusions:

  • The developed automated system significantly enhances apple grading efficiency and accuracy.
  • The proposed models provide a replicable framework for image processing and feature extraction in automated fruit grading.
  • This research offers a standardized approach for grading spherical fruits.