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Pre-trained deep learning-based classification of jujube fruits according to their maturity level

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Abstract

Assessment of crop maturity and quality is pivotal in the food industry and for harvesting. The manual classification of crops based on their maturity levels for harvesting and packaging purpose is a tedious process. However, the emergence of machine learning/deep learning techniques has opened up the ways in this direction, but its practical success is still limited. In this research, we examined two convolutional neural network paradigms (i.e., AlexNet and VGG16) utilizing a transfer-learning approach for classifying the jujube fruits based on their maturity level (i.e., unripe, ripe, and over-ripe). The training/testing of models was performed over the collected dataset of around 400 images, which was further augmented to 4398 images collectively for the three maturity classes. The best accuracy achieved for the correct classification of maturity classes with AlexNet and VGG16 for the actual and augmented images are 94.17% & 97.65%, and 98.26% & 99.17% respectively. The examined models were compared with two existing methods for jujube maturity classification and found to be performing better. The significantly improved success rate of VGG16 models over the AlexNet and existing proposed models for jujube classification makes the model recommendable for developing an efficient system for the automated harvesting and sorting of jujube fruits.

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Abbreviations

ML:

Machine learning

DL:

Deep learning

CNN:

Convolutional neural network

ANN:

Artificial neural network

AlexNet:

Alex network

VGG16:

Visual geometry group (16 Layers)

BPNN:

Back-propagation neural network

KNN:

K-nearest neighbour

DT:

Decision tree

GMM:

Gaussian mixture model

SVM:

Support vector machine

KPCA:

Kernel-principal component Analysis

ELM:

Extreme learning machine

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Acknowledgements

This research work received no specified grant funding from any funding agency, commercial, or not-for-profit sectors. The authors express their gratitude in advance to the reviewers for their significant suggestions and comments.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Dr. APJ Abdul Kalam Technical University, Lucknow, India

    Atif Mahmood & Amod Kumar Tiwari

  2. Department of Computer Application, Dr. APJ Abdul Kalam Technical University, Lucknow, India

    Sanjay Kumar Singh

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  1. Atif Mahmood
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  2. Sanjay Kumar Singh
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  3. Amod Kumar Tiwari
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A.M contemplated the structure of the article, performs the experimental works, and accomplished the paper writing. AKT and SKS were involved in the suggestions and final revision of the article. All authors have read and approved the manuscript.

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Correspondence to Atif Mahmood.

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Mahmood, A., Singh, S.K. & Tiwari, A.K. Pre-trained deep learning-based classification of jujube fruits according to their maturity level. Neural Comput & Applic 34, 13925–13935 (2022). https://doi.org/10.1007/s00521-022-07213-5

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  • DOI: https://doi.org/10.1007/s00521-022-07213-5

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