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

Fruit Development, Structure, and Function

Fruit Development, Structure, and Function

Fruits form from a mature flower ovary. As seeds develop from the ovules contained within, the ovary wall undergoes a series of complex changes to form fruit. In some fruits, such as soybeans, the ovary wall dries; in other fruits, such as grapes, it remains fleshy. In some cases, organs other than the ovary contribute to fruit formation; such fruits are called accessory fruits.

You might also read

Related Articles

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

Sort by

Same author

Cross-channel feature transfer 3D U-Net for automatic segmentation of the perilymph and endolymph fluid spaces in hydrops MRI.

Computers in biology and medicine·2025

Same author

Automated volumetric analysis of the inner ear fluid space from hydrops magnetic resonance imaging using 3D neural networks.

Scientific reports·2024

Same author

Effectiveness of Human-Artificial Intelligence Collaboration in Cephalometric Landmark Detection.

Journal of personalized medicine·2022

Same author

Correction to: Diagnosis of schizophrenia with functional connectome data: a graph-based convolutional neural network approach.

BMC neuroscience·2022

Same author

Diagnosis of schizophrenia with functional connectome data: a graph-based convolutional neural network approach.

BMC neuroscience·2022

Same author

Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network.

Journal of clinical medicine·2021

Search research articles

Related Experiment Video

Updated: Jun 3, 2025

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

Gripping Success Metric for Robotic Fruit Harvesting.

Dasom Seo¹, Il-Seok Oh^1,2

¹Department of Computer Science & Artificial Intelligence, Jeonbuk National University, Jeonju-si 54896, Republic of Korea.

Sensors (Basel, Switzerland)

|January 11, 2025

Summary

A new metric for robotic harvesting evaluates fruit gripping success using bounding box centers and gripper specifications. This method is more intuitive than average precision (AP) for selecting efficient fruit-picking robots.

Keywords:

computer vision grasp detection object detection robot harvesting robotic grasping

More Related Videos

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field

Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field

Published on: July 28, 2023

Related Experiment Videos

Last Updated: Jun 3, 2025

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field

Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field

Published on: July 28, 2023

Area of Science:

Agricultural Robotics
Computer Vision
Machine Learning Evaluation

Background:

Computer vision is crucial for agricultural tasks like robotic harvesting, utilizing object detection to identify fruits.
Current object detection evaluation metrics, such as average precision (AP), are insufficient for robotic harvesting as they do not accurately predict gripping success.

Purpose of the Study:

To propose a novel evaluation metric for robotic harvesting that more accurately assesses gripping success.
To provide a more intuitive metric for selecting efficient object detection models for fruit harvesting robots.

Main Methods:

Developed a new metric using bounding box center coordinates and a margin hyperparameter reflecting gripper specifications.
Evaluated popular object detection models on peach and apple datasets using the proposed metric.

Main Results:

The proposed gripping success metric demonstrated higher intuitiveness and utility in interpreting performance data compared to traditional metrics.
Identified significant differences in gripping success predictions based on bounding box overlap shapes, which AP does not capture.

Conclusions:

The novel gripping success metric offers a more reliable approach for evaluating object detection models in robotic harvesting applications.
This metric aids in the selection of more efficient and effective robotic harvesting systems by better predicting real-world performance.