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

You might also read

Related Articles

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

Sort by

Same author

DBCL-DFNet: Dual-Branch Contrastive Learning for Multi-Omics Dynamic Fusion.

Entropy (Basel, Switzerland)·2026

Same author

Correction to "Light-Induced Transformation from Covalent to Supramolecular Polymer Networks".

ACS macro letters·2026

Same author

Cytoskeleton-Inspired Mechanically Interlocked Catenane Framework Enabling Robust yet Dynamic Polymer Networks.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

DNA Framework Nucleator-Enabled Intelligent Hydrogel Interfaces on Living Cells.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same author

A Case of Nonpedal Subcorneal Haematoma Mimicking Melanoma.

Acta dermato-venereologica·2026

Same author

Zeta potential transition correlates with optimal DNA origami silicification temperature.

Chemical communications (Cambridge, England)·2026

Same journal

PlasmiDB: an open-source and customizable database for plasmid lifecycle management in multi-user, multi-project plant molecular biology laboratories.

Plant methods·2026

Same journal

Establishment of a protoplast isolation and transient transformation system for tung tree (Vernicia fordii).

Plant methods·2026

Same journal

Deep aerenchyma: a transformer-based pipeline for scalable phenotyping of rice root aerenchyma lacunae across environments.

Plant methods·2026

Same journal

Comparative analysis of SP3 and S-Trap sample preparation protocols for proteomic profiling associated with somatic embryogenesis efficiency in Olea europaea L.

Plant methods·2026

Same journal

DAPR-AM-Net: an end-to-end smart farming system powered by dual-attention progressive refinement and adaptive MixUp for explainable tomato leaf disease classification and forecasting.

Plant methods·2026

Same journal

Deep soil layers show the most pronounced genetic variation in wheat root length.

Plant methods·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Melon ripeness detection by an improved object detection algorithm for resource constrained environments.

Xuebin Jing^1,2, Yuanhao Wang^1,2, Dongxi Li³

¹College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China.

|August 16, 2024

Summary

This summary is machine-generated.

This study introduces MRD-YOLO, an efficient AI model for detecting melon ripeness, outperforming existing methods with high accuracy and low computational cost. This technology can be deployed on resource-constrained devices for automated fruit harvesting.

Keywords:

Deep learning Melon Object detection Ripeness detection

More Related Videos

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

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

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Related Experiment Videos

Last Updated: Jun 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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

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

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Area of Science:

Agricultural Technology
Computer Vision
Machine Learning

Background:

Fruit quality is significantly influenced by ripeness, a critical factor in cultivation and harvesting.
Traditional manual detection and experimental analysis methods for fruit ripeness are inefficient and costly.

Purpose of the Study:

To develop a lightweight and efficient method for detecting melon ripeness using an improved object detection algorithm.
To create a comprehensive melon dataset capturing real-world complexities for training and validation.

Main Methods:

Proposed a novel object detection method, MRD-YOLO, integrating MobileNetV3, Slim-neck, and Coordinate Attention.
Developed a large-scale melon dataset from greenhouse environments, including occlusions, varying light, and overlapping fruit.
Utilized a lightweight backbone and attention mechanism for enhanced efficiency.

Main Results:

MRD-YOLO achieved a mean Average Precision of 97.4% on the custom dataset, demonstrating high accuracy.
The model requires only 4.8 G FLOPs and 2.06 M parameters, significantly reducing computational load compared to baseline models.
Achieved a mean Average Precision of 85.9% on external datasets, indicating strong generalization capabilities and real-time inference.

Conclusions:

The developed MRD-YOLO method offers an efficient solution for melon ripeness detection.
The created dataset and detection method serve as a valuable reference for fruit ripeness detection across various fruit types.