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Related Experiment Video

Updated: Jun 11, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Rating pome fruit quality traits using deep learning and image processing.

Nhan H Nguyen¹, Joseph Michaud², Rene Mogollon^1,3

¹Department of Horticulture Washington State University Pullman WA USA.

|October 10, 2024

Summary

Granny, an open-source tool, uses machine learning and image processing to objectively assess pome fruit quality, reducing rater bias and improving data resolution for apples and pears.

Keywords:

machine learning pome fruit trait prediction

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Last Updated: Jun 11, 2025

Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Fruit Volatile Analysis Using an Electronic Nose

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Published on: March 30, 2012

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

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Published on: March 16, 2010

Area of Science:

Horticultural Science
Agricultural Technology
Computer Vision

Background:

Pome fruit quality assessment is crucial for harvest timing and storage monitoring.
Traditional visual assessments are subjective, prone to rater bias, and lack objective criteria.
Ensuring consistency in quality ratings across different labs and experiments is challenging.

Purpose of the Study:

To introduce Granny, a novel tool for objective pome fruit quality assessment.
To address limitations of traditional visual assessment methods, including rater bias and low resolution.
To provide backward compatibility with established quality rating standards.

Main Methods:

Utilizes machine learning and image processing algorithms for fruit quality analysis.
Automates the assessment of starch content, peel defects, and peel color.
Designed for objective and high-resolution fruit quality data collection.

Main Results:

Granny effectively reduces rater bias in fruit quality assessments.
The tool provides improved resolution for analyzing fruit attributes like starch content and peel characteristics.
Offers ratings compatible with existing standards, ensuring research continuity.

Conclusions:

Granny enhances the objectivity and resolution of pome fruit quality assessment.
Facilitates integrative analyses by linking fruit quality data with omics and environmental factors.
The open-source availability of Granny promotes wider adoption and research advancement in fruit science.