THE INDEPENDENT RESEARCH REGISTRY FOR FOOD, NUTRITION AND HEALTH

 


Nutrition, Data Science and Artificial Intelligence

The Nutrition, Data Science, and Artificial Intelligence special collection aims to explore the intersection of nutrition science, data analytics, and AI. We invite submissions that leverage advanced computational techniques to analyse large-scale dietary data, predict nutritional outcomes, and develop innovative solutions for personalised dietary recommendations and dietary pattern analysis.
 

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  • Study Status: Published
  • Study Type: Review
  • Study Location: Global

Study Title
Computer Vision and Machine Learning Based Approaches for Food Security: A Review

Principal Investigator
Shivani Sood, Harjeet Singh

Affiliation
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

Start Date
Not specified

End Date
Not specified

Study Objective
To explore the applications of computer vision (CV) and machine learning (ML) techniques for improving food security, addressing challenges such as food shortages, quality reduction, wastage, and loss of food products.

Short Abstract
This review addresses various computer vision and machine learning-based techniques applied in the food production and agriculture field to combat food security issues. The study focuses on image processing-based applications such as fruit sorting, disease prediction, and soil quality assessment, concluding that deep learning (DL) approaches provide the best results, particularly for image processing tasks.

Study Design
Systematic review

Population
Global agriculture and food systems

Sample Size
Not applicable

Inclusion Criteria
Studies focused on the use of machine learning and computer vision for food security, including applications like disease detection, fruit sorting, soil quality measurement, etc.

Exclusion Criteria
Not specified

Intervention/Exposure
Machine learning and computer vision techniques, including deep learning for image processing applications in food security

Outcome Measures
Improved food production, disease prediction, better agricultural decision-making, and food security

Funding Source
Not specified

Collaborating Institutions
Chitkara University Institute of Engineering and Technology, Chitkara University

Ethics Approval
Not specified

Publication Status
Published in Multimedia Tools and Applications (2021).

Keywords
Food security, deep learning, computer vision, machine learning, smart farming

Data Collection Methods
Systematic review of machine learning and computer vision applications in food security

Primary Data Availability
Not applicable

Contact Information
Harjeet Singh: harjeet.singh@chitkara.edu.in