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Strawberry Fruit Disease Identification Using Digital Image Processing Using GLCM With Artificial Neural Network Method
Abstract
Purpose: This research aims to identify strawberry fruit diseases using digital image processing using GLCM with the backpropagation artificial neural network method.
Design/methodology/approach: Using images that have been preprocessed grayscale, crop, and resize and then processed using GLCM for traning using backpropagation artificial neural networks.
Findings/result: Based on 250 image data that is processed by GLCM and classified using a backpropagation artificial neural network, it can be concluded that the best accuracy rate is obtained from ReLU activation with a traning data accuracy value of 95% and test data accuracy of 54%.
Originality/value/state of the art: This research uses a combination of primary data with kaggle data by using a comparison of several experiments by changing the loss, optimizer and activation parameters.Keywords
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DOI: https://doi.org/10.31315/telematika.v21i1.9861
DOI (PDF): https://doi.org/10.31315/telematika.v21i1.9861.g6672
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Status Kunjungan Jurnal Telematika