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

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

You might also read

Related Articles

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

Sort by
Same author

Ohmic Contact Resistance in Wide-Bandgap and Ultrawide-Bandgap Power Semiconductors: From Fundamental Physics to Interface Engineering.

Materials (Basel, Switzerland)·2026
Same author

Investigating unusual photoluminescence from the excited state in a DCM derivative and its application in lighting devices.

Physical chemistry chemical physics : PCCP·2025
Same author

Ground-based imagery dataset for early weed classification in tomato crops.

Data in brief·2025
Same author

Intelligent Inter- and Intra-Row Early Weed Detection in Commercial Maize Crops.

Plants (Basel, Switzerland)·2025
Same author

Development and testing of a precision hoeing system for re-compacted ridge tillage in maize.

Heliyon·2024
Same author

Flexible and highly selective NO<sub>2</sub> gas sensor based on direct-ink-writing of eco-friendly graphene oxide for smart wearable application.

Chemosphere·2024

Related Experiment Video

Updated: May 11, 2026

Development of an Algorithm to Perform a Comprehensive Study of Autonomic Dysreflexia in Animals with High Spinal Cord Injury Using a Telemetry Device
06:51

Development of an Algorithm to Perform a Comprehensive Study of Autonomic Dysreflexia in Animals with High Spinal Cord Injury Using a Telemetry Device

Published on: July 29, 2016

Development and testing of a decision making based method to adjust automatically the harrowing intensity.

Victor Rueda-Ayala1, Martin Weis, Martina Keller

  • 1Department of Weed Science (360b), University of Hohenheim, 70599 Stuttgart, Germany. patovicnsf@gmail.com

Sensors (Basel, Switzerland)
|May 15, 2013
PubMed
Summary

Optimizing harrowing intensity using site-specific data improves weed control in wheat and barley. This variable intensity approach enhances weed reduction and crop yield compared to constant harrowing methods.

Related Experiment Videos

Last Updated: May 11, 2026

Development of an Algorithm to Perform a Comprehensive Study of Autonomic Dysreflexia in Animals with High Spinal Cord Injury Using a Telemetry Device
06:51

Development of an Algorithm to Perform a Comprehensive Study of Autonomic Dysreflexia in Animals with High Spinal Cord Injury Using a Telemetry Device

Published on: July 29, 2016

Area of Science:

  • Agricultural Engineering
  • Precision Agriculture
  • Weed Management

Background:

  • Harrowing is a common weed control method, typically applied uniformly across fields.
  • Optimizing harrowing efficacy requires accounting for site-specific soil, weed, and crop conditions.
  • Current methods often lack adaptability to field variability.

Purpose of the Study:

  • To develop and validate an algorithm for automatic adjustment of harrowing intensity.
  • To investigate the impact of variable harrowing intensity on weed density, crop cover, and yield.
  • To assess the feasibility of real-time harrowing adjustment.

Main Methods:

  • Utilized geo-referenced sensors (bispectral cameras, load cells) to acquire field data on crop cover, weed density, and soil density.
  • Developed a linguistic fuzzy inference system (LFIS) integrating sensor data with optimal harrowing intensity rules.
  • Evaluated the system in field experiments comparing constant versus variable harrowing intensities.

Main Results:

  • Variable harrowing intensity achieved greater weed density reduction than constant intensity.
  • Adjusting harrowing intensity slightly reduced crop leaf cover but improved crop yield.
  • Real-time adjustment is feasible with appropriate sensor placement.

Conclusions:

  • Site-specific adjustment of harrowing intensity offers superior weed management in cereals.
  • Precision harrowing systems can enhance both weed control and crop productivity.
  • The developed LFIS algorithm shows promise for adaptive weed harrowing technology.