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

Vision01:24

Vision

59.3K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
59.3K

You might also read

Related Articles

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

Sort by
Same author

Preliminary Evaluation of New Wearable Sensors to Study Incongruous Postures Held by Employees in Viticulture.

Sensors (Basel, Switzerland)·2024
Same author

GC-MS and Sensory Analysis of Aqueous Extracts of Monovarietal American Hops, Produced Using the Synergy Pure™ Extraction Technique.

Foods (Basel, Switzerland)·2024
Same author

Comprehension of drying kinetics and stone milling process of chestnuts: a focus on moisture content, milling speed, and energy evaluation.

Journal of the science of food and agriculture·2024
Same author

The Effects of Storage Time and Environmental Storage Conditions on Flour Quality, Dough Rheology, and Biscuit Characteristics: The Case Study of a Traditional Italian Biscuit (Biscotto di Prato).

Foods (Basel, Switzerland)·2022
Same author

The Use of Infrared Thermography for the Monitoring of Udder Teat Stress Caused by Milking Machines.

Animals : an open access journal from MDPI·2019
Same author

First Evaluation of Infrared Thermography as a Tool for the Monitoring of Udder Health Status in Farms of Dairy Cows.

Sensors (Basel, Switzerland)·2018

Related Experiment Video

Updated: Jan 11, 2026

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
09:29

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

Published on: February 11, 2014

13.5K

Preliminary Study of Vision-Based Artificial Intelligence Application to Evaluate Occupational Risks in Viticulture.

Sirio R S Cividino1,2, Alessio Cappelli1, Paolo Belluco3

  • 1Department of Human Science and Quality of Life Promotion, San Raffaele Telematic University, Via Val Cannuta 247, 00166 Rome, Italy.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary

Vision-based Artificial Intelligence (AI) shows high agreement with human experts for detecting agricultural safety risks in viticulture. This technology offers reliable, standardized risk detection for enhanced occupational health and safety.

Keywords:
agricultural machineryartificial intelligencecomputer visionergonomicsexplainable AIoccupational health and safetyrisk assessmentviticulture

More Related Videos

Vision Training Methods for Sports Concussion Mitigation and Management
12:54

Vision Training Methods for Sports Concussion Mitigation and Management

Published on: May 5, 2015

18.0K
Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

856

Related Experiment Videos

Last Updated: Jan 11, 2026

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
09:29

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

Published on: February 11, 2014

13.5K
Vision Training Methods for Sports Concussion Mitigation and Management
12:54

Vision Training Methods for Sports Concussion Mitigation and Management

Published on: May 5, 2015

18.0K
Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

856

Area of Science:

  • Agricultural Safety
  • Occupational Health
  • Artificial Intelligence in Agriculture

Background:

  • Viticulture presents significant occupational hazards, including repetitive tasks, pesticide exposure, and machinery use.
  • Existing safety assessments often rely on manual evaluations, which can be subjective and time-consuming.

Purpose of the Study:

  • To evaluate the coherence of vision-based Artificial Intelligence (AI) systems with human expert assessments for occupational health and safety in viticulture.
  • To determine the reliability and accuracy of AI in identifying risks related to manual work, environments, and machinery.

Main Methods:

  • A dataset of 203 annotated vineyard images was analyzed by both safety professionals and an AI pipeline.
  • The AI pipeline integrated convolutional neural networks, regulatory contextualization, and risk matrix evaluation.
  • Agreement was quantified using weighted Cohen's Kappa, with statistical tests comparing AI and expert classifications.

Main Results:

  • AI achieved high agreement with human experts (weighted Cohen's Kappa: 0.94-0.96) and low overall error rates (<14%).
  • No significant differences were found between AI and expert classifications.
  • Errors were mainly false negatives in machinery images, indicating areas for AI improvement.

Conclusions:

  • Vision-based AI systems provide reliable and standardized risk detection in viticulture, comparable to human experts.
  • AI integration, complemented by other sensors and regulatory frameworks, can advance proactive and preventive safety management in agriculture.
  • Further development is needed for AI explainability and improved sensitivity in complex scenarios.