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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

172
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
172
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.8K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.8K
Multiple Regression01:25

Multiple Regression

3.1K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.1K
Light Acquisition02:16

Light Acquisition

8.6K
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.
8.6K
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

906
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
906
Plant Breeding and Biotechnology01:59

Plant Breeding and Biotechnology

19.7K
Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
19.7K

You might also read

Related Articles

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

Sort by
Same author

Prototype of Monitoring Transportation Pollution Spikes through the Internet of Things Edge Networks.

Sensors (Basel, Switzerland)·2023
Same journal

Correction to "Mathematical Modelling of COVID-19 Transmission in Kenya: A Model with Reinfection Transmission Mechanism".

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Ligustrazine Inhibits Lung Phosphodiesterase Activity in a Rat Model of Allergic Asthma.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Delivery of miR-224-5p by Exosomes from Cancer-Associated Fibroblasts Potentiates Progression of Clear Cell Renal Cell Carcinoma.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Empirical Analysis of the Nursing Effect of Intelligent Medical Internet of Things in Postoperative Osteoarthritis.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Evaluation and Analysis of the Intervention Effect of Systematic Parent Training Based on Computational Intelligence on Child Autism.

Computational and mathematical methods in medicine·2024
Same journal

RETRACTION: Humanistic Spirit Training of Medical Students Based on Multisource Medical Data Fusion.

Computational and mathematical methods in medicine·2024
See all related articles

Related Experiment Video

Updated: Aug 29, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning

Ashay Rokade1, Manwinder Singh1, Sandeep Kumar Arora1

  • 1School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India.

Computational and Mathematical Methods in Medicine
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

The Internet of Things (IoT) and machine learning enhance intelligent farming by optimizing crop production. A novel system using supervised learning, particularly Support Vector Machines (SVM), improves classification accuracy for better crop yield.

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

464
A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

11.7K

Related Experiment Videos

Last Updated: Aug 29, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

464
A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

11.7K

Area of Science:

  • Agricultural Technology
  • Data Science
  • Machine Learning

Background:

  • The Internet of Things (IoT) is vital for modern agriculture, enabling innovative practices to increase crop yield with existing resources.
  • Intelligent farming systems leverage IoT infrastructure, including sensors, actuators, and network connectivity, to optimize agricultural processes.
  • Machine learning and IoT data analytics offer significant potential for improving both the quantity and quality of crop output to meet global food demand.

Purpose of the Study:

  • To develop an intelligent farming system integrating medical informatics and predictive data analytics for enhanced crop production.
  • To implement a supervised machine learning approach within an IoT framework to optimize farming conditions based on plant needs.
  • To investigate the effectiveness of machine learning algorithms in regulating actuators and improving organic farming practices.

Main Methods:

  • A four-layer architecture comprising cloud, fog, edge, and sensor layers was proposed for the intelligent farming system.
  • Machine learning was applied to sensor data for actuator regulation, with an analytics and decision-making system at the fog layer.
  • Two supervised machine learning approaches, Support Vector Machine (SVM) and Artificial Neural Network (ANN) (classification and regression), were employed and evaluated.

Main Results:

  • The system utilizes predictive data analytics on sensing parameters within an intelligent agricultural system.
  • Experimental results analyzed in MATLAB indicate that Support Vector Machine (SVM) achieves superior classification accuracy compared to Artificial Neural Network (ANN).
  • The proposed system demonstrates improved performance over other state-of-the-art methods in classification accuracy.

Conclusions:

  • The integration of IoT, machine learning, and a layered architecture (cloud, fog, edge, sensor) significantly enhances intelligent farming.
  • Supervised learning, particularly SVM, proves effective for predictive analytics and decision-making in optimizing crop production and quality.
  • The developed system offers a promising approach for boosting agricultural utility and achieving organic farming goals.