Image Classification of Vegetable Quality using Support Vector Machine
based on Convolutional Neural Network

Image Classification of Vegetable Quality using Support Vector Machine
based on Convolutional Neural Network

vegetable quality; image classification; convolutional neural network; support vector machine; feature extration

As part of an effort to develop intelligent agriculture, new methods for enhancing the quality of vegetables are
being continually developed. In recent years, the Convolutional Neural Network (CNN) has shown to be the most
successful and extensively used approach for identifying the quality of pre-trained vegetables. However, this
method is time-consuming due to the scarcity of truly large, significant datasets. Using a pre-trained CNN model
as a feature extractor is a straightforward method for utilizing CNNs' capabilities without investing time in
training. While, Support Vector Machine (SVM excels at processing data with tiny dimensions and significantly
larger instances. SVM more accurately classifies the flatten/vector feature supplied by the CNN fully connected
layer with small dimensions. In addition, implementing Data Augmentation (DA) and Weighted Class (WC) for
data variety and class imbalance reduction can improve CNN-SVM performance. The research results show
highest accuracy during training always achieves 100% across all experimental options. With an average
accuracy of 69.66% in the testing process and 92.51% in the prediction process for all data, the experimental
findings demonstrate that CNN-SVM outperforms CNN in terms of accuracy performance in all possible
experiments, with or without WC and or DA approach.

Hanny Nurrani1
, Andi Kurniawan Nugroho2*
, Sri Heranurweni3

Universitas Semarang, Semarang, Indonesia

05-02-2023

Fajar bagus W

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Indonesia

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