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Related Experiment Video

Updated: Aug 13, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review.

Preety Baglat^1,2, Ahatsham Hayat^1,2, Fábio Mendonça^1,2

¹University of Madeira, 9000-082 Funchal, Portugal.

Sensors (Basel, Switzerland)

|January 21, 2023

Summary

This summary is machine-generated.

Automating banana ripeness analysis is crucial for nutrient composition and demand. This review highlights sensor cameras and color features for accurate ripeness prediction, favoring four stages and specific machine learning models.

Keywords:

banana computer imaging deep learning machine learning ripeness

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Last Updated: Aug 13, 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

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

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Published on: March 28, 2025

Area of Science:

Agricultural Science
Computer Vision
Food Science

Background:

Banana ripeness significantly impacts nutrient content and market demand.
Traditional ripeness assessment requires expert knowledge and manual labor.
Automated methods are being developed to reduce human intervention in ripeness analysis.

Conclusions:

Existing studies face limitations including insufficient dataset and device information, limited data availability, and underutilization of data augmentation.
Future research should address these shortcomings and involve expert collaboration for robust ripeness prediction.
Developing standardized datasets and capturing protocols is essential for advancing automated banana ripeness assessment.