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

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.

You might also read

Related Articles

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

Sort by

Same author

Distinguishing a Drone from Birds Based on Trajectory Movement and Deep Learning.

Sensors (Basel, Switzerland)·2026

Same author

Neural Network Method of Analysing Sensor Data to Prevent Illegal Cyberattacks.

Sensors (Basel, Switzerland)·2025

Same author

Helicopter Turboshaft Engines' Neural Network System for Monitoring Sensor Failures.

Sensors (Basel, Switzerland)·2025

Same author

Intelligent Method of Identifying the Nonlinear Dynamic Model for Helicopter Turboshaft Engines.

Sensors (Basel, Switzerland)·2024

Same author

Methods and Software Tools for Reliable Operation of Flying LiFi Networks in Destruction Conditions.

Sensors (Basel, Switzerland)·2024

Same author

Neural Network Signal Integration from Thermogas-Dynamic Parameter Sensors for Helicopters Turboshaft Engines at Flight Operation Conditions.

Sensors (Basel, Switzerland)·2024

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Home
Intelligent Integrated System For Fruit Detection Using Multi-uav Imaging And Deep Learning.

Home
Intelligent Integrated System For Fruit Detection Using Multi-uav Imaging And Deep Learning.

Related Experiment Video

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

Intelligent Integrated System for Fruit Detection Using Multi-UAV Imaging and Deep Learning.

Oleksandr Melnychenko¹, Lukasz Scislo², Oleg Savenko¹

¹Faculty of Information Technologies, Khmelnytskyi National University, 11, Instytuts'ka Str., 29016 Khmelnytskyi, Ukraine.

Sensors (Basel, Switzerland)

|March 28, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces an AI and deep learning system using multiple drones for real-time fruit detection and counting in orchards. The innovative approach achieves high accuracy, improving agricultural efficiency for Industry 4.0.

Keywords:

YOLOv5 deep learning fruit detection fruit yield estimation synchronization and autonomous movement unmanned aerial vehicle video stream transmission

More Related Videos

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

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Related Experiment Videos

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

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

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Agricultural Technology
Computer Vision
Artificial Intelligence

Background:

Industry 4.0 demands enhanced agricultural efficiency through intelligent sensors and computing.
Traditional fruit detection methods lack the precision and timeliness needed for modern orchard management.
Technological integration is vital for the evolving agricultural sector.

Purpose of the Study:

To develop a novel, AI-driven system for accurate, real-time fruit detection and counting in orchards.
To leverage multi-unmanned aerial vehicle (UAV) systems for synchronized data capture and image processing.
To improve orchard management and harvest preparation through advanced digital agriculture solutions.

Main Methods:

Integration of artificial intelligence (AI) and deep learning (DL) with multi-UAV platforms.

Simultaneous capture and synchronization of video frames from multiple UAV cameras.

Image quality optimization for high-resolution object detection in dynamic environments.

Main Results:

Achieved a mean average precision of 86.8% for fruit detection and counting.
Maintained low error rates: 14.7% false positive and 18.3% false negative.
Demonstrated effectiveness under challenging weather conditions, including cloudiness.

Conclusions:

The multi-UAV imaging and DL approach offers superior real-time fruit recognition capabilities.
This technology represents a significant advancement for digital agriculture and Industry 4.0 objectives.
The system provides crucial data for efficient orchard management and harvest planning.