Identify the Condition of Corn Plants Using Gray Level Co-occurrence Matrix and Bacpropagation

Abd Mizwar A. Rahim; Theopilus Bayu Sasongko

doi:10.30812/matrik.v24i2.4035

Abd Mizwar A. Rahim Universitas AMIKOM, Yogyakarta, Indonesia
Theopilus Bayu Sasongko Universitas AMIKOM, Yogyakarta, Indonesia

DOI: https://doi.org/10.30812/matrik.v24i2.4035

Keywords: Corn Plant Diseases., Machine Learning, Artificial Neural Networks., Backpropagation, Gray Level Co-occurrence Matrix

Abstract

This research aims to increase the accuracy of identifying the condition of corn plants based on leaf features using the GLCM and ANN Backpropagation methods. The GLCM method is used to extract features from corn leaf images, while Backpropagation ANN is used to classify the condition of corn plants based on these features. This classification was carried out using a dataset of corn leaves from four different conditions, namely healthy, leaf-spot, leaf-blight, and leaf-rust. Next, leaf features are extracted using the GLCM method. After that, data normalization was carried out, balancing the dataset, and training was carried out on the Backpropagation ANN model to classify the condition of the corn plants. After training the model, the next model evaluation is carried out using the confusion matrix method. The research results show that the method used can produce quite high accuracy when identifying the condition of corn plants, with an accuracy of 99%. This shows that the use of GLCM and ANN Backpropagation can be a good alternative in identifying the condition of corn plants. This research provides benefits in making it easier to accurately identify the condition of corn plants.

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