Gray Level Co-Occurrence Matrix (GLCM)-based Feature Extraction for Rice Leaf Diseases Classification

Herminarto Nugroho; Wahyu Agung Pramudito; Handoyo Suryo Laksono

doi:10.12928/biste.v6i4.9286

Authors

Herminarto Nugroho Universitas Pertamina
Wahyu Agung Pramudito Universitas Pertamina
Handoyo Suryo Laksono Universitas Pertamina

DOI:

https://doi.org/10.12928/biste.v6i4.9286

Keywords:

Gray Level Co-Occurance Matrix, Artificial Intelligence, Feature Extraction, Neural Network, Image Classification

Abstract

In this paper, we propose Gray Level Co-Occurrence Matrix (GLCM) based Feature Extraction to identify and classify rice leaf diseases. An Artificial Neural Network (ANN) algorithm is used to train a classification model. Various statistical features such as energy, contrast, homogeneity, and correlation are extracted from the GLCM matrix to describe the image texture features. After feature removal, an ANN classification model was trained using a dataset consisting of images of healthy and diseased rice leaves. The ANN training process involves optimizing weights and bias using backpropagation to achieve accurate classification. After training, the ANN model is tested using split test data to measure classification performance. The experimental results show that the GLCM method is effective in helping improve accuracy, validation of accuracy, loss, validation of loss, precision, and recall.

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