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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.4K
Reducing Line Loss01:18

Reducing Line Loss

217
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
217
Apparent Weight01:09

Apparent Weight

9.0K
True weight is the measure of the gravitational force acting on an object. However, if the object accelerates, its measured weight is different from its true weight. Similar observations can be made when the object is submerged in water. An object's weight in water is its apparent weight, which is equal to the difference between its true weight and the buoyant forces.
Consider a person standing on a bathroom scale inside an elevator. If the scale is accurate at rest, its reading equals the...
9.0K
Optimal Foraging00:48

Optimal Foraging

12.6K
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.
12.6K
Light Acquisition02:16

Light Acquisition

8.7K
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.
8.7K

You might also read

Related Articles

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

Sort by
Same author

Integrative multivariate genomic analysis reveals shared genetic determinants and druggable targets for vascular calcification.

Frontiers in medicine·2026
Same author

A Spectral Reflectance Model of Smooth Dry Soil Surfaces for Varied Soil Properties Based on Intelligent Learning.

Sensors (Basel, Switzerland)·2026
Same author

The diagnostic value of miR-338-3p in acute coronary syndrome and its correlation with MACE occurrence.

Journal of cardiothoracic surgery·2026
Same author

Metabolic reprogramming-a breakthrough point in overcoming resistance to BRAF mutant melanoma targeted therapy (Review).

Oncology letters·2026
Same author

Optimizing the application of pig manure in crop rotation systems: a comprehensive assessment of crop yield, quality, and soil safety.

BMC plant biology·2026
Same author

Optimizing water and organic fertilizer use to enhance seedling growth and soil health in Chinese cabbage (Brassica rapa spp. pekinensis) cultivation.

BMC plant biology·2025

Related Experiment Video

Updated: Oct 14, 2025

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

9.5K

Lightweight Fruit-Detection Algorithm for Edge Computing Applications.

Wenli Zhang1, Yuxin Liu1, Kaizhen Chen1

  • 1Department of Information, Beijing University of Technology, Beijing, China.

Frontiers in Plant Science
|November 1, 2021
PubMed
Summary
This summary is machine-generated.

A new lightweight deep learning algorithm enables real-time fruit detection on edge devices for modern horticulture. This AI advancement overcomes processing limitations, improving agricultural applications with high accuracy and speed.

Keywords:
deep learningedge devicesfruit detectionlightweightmodern horticulture

More Related Videos

Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field
04:21

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

Published on: July 28, 2023

2.2K
Fruit Volatile Analysis Using an Electronic Nose
11:02

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

22.0K

Related Experiment Videos

Last Updated: Oct 14, 2025

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

9.5K
Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field
04:21

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

Published on: July 28, 2023

2.2K
Fruit Volatile Analysis Using an Electronic Nose
11:02

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

22.0K

Area of Science:

  • Computer Vision
  • Artificial Intelligence in Agriculture
  • Edge Computing

Background:

  • Deep learning excels in fruit detection for horticulture, but deployment on edge devices is limited by processing power.
  • Current limitations hinder the integration of AI algorithms in real-time agricultural applications.
  • Edge device constraints present a significant bottleneck for AI-driven horticultural advancements.

Purpose of the Study:

  • To develop a lightweight fruit-detection algorithm optimized for edge devices in horticulture.
  • To enable real-time AI-powered fruit detection with high accuracy on resource-constrained hardware.
  • To address the bottleneck of low image processing capability in deploying AI for modern agriculture.

Main Methods:

  • Proposed a novel lightweight algorithm using Light-CSPNet as the backbone.
  • Incorporated an improved feature-extraction module, a specialized down-sampling method, and a feature-fusion module.
  • Tested the algorithm on NVIDIA Jetson Xavier NX, TX2, and NANO edge devices for performance evaluation.

Main Results:

  • Achieved average detection precisions of 0.93 (orange), 0.847 (tomato), and 0.850 (apple).
  • Demonstrated real-time detection speeds: up to 24.8 FPS on Jetson Xavier NX, 14.5 FPS on Jetson TX2, and 8.5 FPS on Jetson NANO.
  • The algorithm offers a component add/remove function for flexible trade-offs between accuracy and speed.

Conclusions:

  • The proposed lightweight algorithm successfully enables real-time fruit detection on edge devices.
  • The algorithm maintains high fruit-detection accuracy while overcoming edge device processing limitations.
  • This AI solution enhances the practical utilization of AI algorithms in modern horticulture.