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

Light Acquisition02:16

Light Acquisition

8.4K
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.
8.4K

You might also read

Related Articles

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

Sort by
Same author

Neoadjuvant leukocyte interleukin injection immunotherapy improves overall survival in low-risk locally advanced head and neck squamous cell carcinoma -the <i>IT-MATTERS</i> study.

Pathology oncology research : POR·2025
Same author

Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological Data.

Sensors (Basel, Switzerland)·2024
Same author

The relationship between working in the "gig" economy and perceived subjective well-being in Western Balkan countries.

Frontiers in psychology·2023
Same author

Prediction of Pest Insect Appearance Using Sensors and Machine Learning.

Sensors (Basel, Switzerland)·2021
Same author

Semiconductor Gas Sensors: Materials, Technology, Design, and Application.

Sensors (Basel, Switzerland)·2020
Same author

Advanced Signal Processing and Adaptive Learning Methods.

Computational intelligence and neuroscience·2019
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
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
See all related articles

Related Experiment Video

Updated: Jun 11, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

725

Image Processing for Smart Agriculture Applications Using Cloud-Fog Computing.

Dušan Marković1, Zoran Stamenković2,3, Borislav Đorđević4

  • 1Faculty of Agronomy in Čačak, University of Kragujevac, Cara Dušana 34, 32102 Čačak, Serbia.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for smart agriculture, optimizing image classification on edge devices. This enhances early problem detection and resource management, improving efficiency and reducing costs in crop monitoring.

Keywords:
agriculture applicationcloud-fog computingdeep learningimage classification

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
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

3.8K

Related Experiment Videos

Last Updated: Jun 11, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

725
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
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

3.8K

Area of Science:

  • Agricultural Technology
  • Computer Science
  • Artificial Intelligence

Background:

  • The Internet of Things (IoT) generates vast data, necessitating advanced analytical solutions, particularly in smart agriculture for crop monitoring.
  • Continuous monitoring of crop growth aids in timely interventions for disease, weed, and pest control, boosting agricultural productivity and sustainability.
  • Image analysis using Convolutional Neural Networks (CNNs) offers significant potential for enhancing decision-making systems in smart agriculture.

Purpose of the Study:

  • To develop a deep learning model for image classification optimized for resource-constrained Fog computing devices.
  • To enable early problem detection and optimize resource management in smart agriculture through efficient image processing.
  • To reduce agricultural operating costs and manual labor by leveraging edge and fog computing for data processing.

Main Methods:

  • Implementation of a Fog computing architecture connecting Cloud and Edge devices for data processing.
  • Development and optimization of a deep learning model for image classification suitable for hardware-limited devices.
  • Testing a tomato disease classification model on Field-Programmable Gate Arrays (FPGAs) to evaluate performance trade-offs.

Main Results:

  • The proposed solution effectively off-loads data processing to Edge and Fog devices, improving system responsiveness and reliability.
  • Significant reductions in data transmission and storage costs were achieved.
  • The optimized model for FPGA execution showed a minimal decrease in test accuracy (0.83%) for tomato disease classification, maintaining high performance (95.46%).

Conclusions:

  • The developed deep learning model is adaptable for implementation on resource-limited Fog computing devices, enhancing smart agriculture applications.
  • Optimizing image processing at the edge significantly improves system efficiency, reduces costs, and increases overall reliability and security.
  • The approach demonstrates a viable method for balancing model size and accuracy, crucial for deploying AI in edge environments for agriculture.