Title

Size classification of tomato fruit using thresholding, machine learning and deep learning techniques

College

Gokongwei College of Engineering

Department/Unit

Manufacturing Engineering and Management

Document Type

Article

Source Title

Agrivita

Volume

41

Issue

3

First Page

586

Last Page

596

Publication Date

1-1-2019

Abstract

The size of tomato fruits is closely related to the market segment and price. Manual sorting in tomato is very dependent on human interpretation and thus, very prone to error. The study presents thresholding, machine learning and deep learning techniques in classifying the tomato as small, medium and large based from a single tomato fruit image implemented using Open CV libraries and Python programming. Tomato images with different sizes are gathered where features like area, perimeter and enclosed circle radius are extracted. The experiment shows that using thresholding, a classification accuracy of 85.83%, 65.83% and 80% was achieved for area, perimeter and enclosed circle radius, respectively. For machine learning, the training accuracy rates were recorded as 94.00%-95.00% for SVM, 97.50-92.50% for KNN and 90.33-92.50% for ANN. Comparison of models revealed that SVM is the most model without over fitting. The deep learning approach, regardless of the algorithm, produced low performances with 82.31%-78.21%-55.97% training-validation-testing accuracy for VGG16, 48.17%-41.44%-37.64% for InceptionV3 and 56.05%-44.96%-22.78% for ResNet50 models. Comparative analysis showed that machine learning technique bested the performance of the thresholding and deep learning techniques in classifying the tomato fruit size in terms of accuracy performance. © 2019, Agriculture Faculty Brawijaya University. All rights reserved.

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Digitial Object Identifier (DOI)

10.17503/agrivita.v41i3.2435

Disciplines

Manufacturing

Keywords

Image processing; Size perception; Computer vision; Tomatoes—Size

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