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Machine learning and deep learning models applied to identification and classification of mango

Nguyen Duc Phong Nguyen Manh Son Nguyen Manh Ha Bui Xuan Thanh Ta Thi Thao Nguyen Thi Van Anh Le Thi Hong Hao Nguyen Duc Thanh
Received: 19 Jul 2024
Revised: 07 Sep 2024
Accepted: 09 Sep 2024
Published: 30 Sep 2024

Article Details

How to Cite
Nguyen Duc Phong, Nguyen Manh Son, Nguyen Manh Ha, Bui Xuan Thanh, Ta Thi Thao, Nguyen Thi Van Anh, Le Thi Hong Hao, Nguyen Duc Thanh. "Machine learning and deep learning models applied to identification and classification of mango". Vietnam Journal of Food Control. vol. 7, no. 3, pp. 429-437, 2024
PP
429-437
Counter
24

Main Article Content

Abstract

This study utilizes the data published on the website https://data.mendeley.com/ datasets/46htwnp833/2, which includes visible-near-infrared (Vis-NIR) spectral data at wavelengths ranging from 309 nm to 1149 nm for 11691 mangoes in Australia, collected from 10 mango varieties across 2 different growing regions. The research developed machine learning models with open-source programming language Python such as: principal component analysis (PCA) combined with support vector machines (SVM), decision trees (DT), random forests (RF), and artificial neural networks (ANN); partial least squares model combined with discriminant analysis (PLS-DA); and a deep learning model 1-dimensional convolutional neural network (1D-CNN). The preprocessing steps were caried out based on the full spectral data with second derivative, smoothing using the Savitzky-Golay algorithm, and data balancing via a new Synthetic Minority Oversampling Technique (SMOTE). The results demonstrated that applying the SMOTE data preprocessing technique before running the machine learning models significantly enhanced classification accuracy. Furthermore, using a 1D-CNN model with a complex structure provided higher classification efficiency than conventional machine learning models. The accuracy of the 1D-CNN model in classifying mango ripeness, mango variety, and growing location was 99.40%, 94.35%, and 96.92%, respectively. The 1D-CNN deep learning model is well-suited for sample classification when dealing with large datasets containing tens of thousands of samples based on spectral data.

Keywords:

mango classification, machine learning, deep learning, 1D-CNN, VisNIR spectra

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