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. 2023 Jan 9;23(2):738.
doi: 10.3390/s23020738.

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 , Ankit Gupta  1   2 , Sheikh Shanawaz Mostafa  2 , Fernando Morgado-Dias  1   2
Affiliations

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

Preety Baglat et al. Sensors (Basel). .

Abstract

The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued.

Keywords: banana; computer imaging; deep learning; machine learning; ripeness.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
PRISMA flow diagram for banana ripeness.
Figure 2
Figure 2
Banana ripeness detection using an ML pipeline.
Figure 3
Figure 3
Types of capturing devices used for the detection of banana ripeness studies (* represented the study approaches in all three categories).
Figure 4
Figure 4
Classification and feature-based methods.
See this image and copyright information in PMC

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