Ibrahimy University Situbondo Classification of Potato Plant Diseases Based on Leaf and Stem Images Using Convolutional Neural Network and Gray Level Co-Occurrence Matrix Methods
DOI:
https://doi.org/10.33379/gtech.v8i3.4298Keywords:
potato, cnn, glcm, Disease classificationAbstract
The factor that causes potato plants to be less than optimal is diseased potato plants. This potato plant disease can be identified from spotty leaves and dry stems, by identifying it using an identification system based on disease images. Potato stem datasets were obtained at the Ijen Bondowoso plantation as many as 1,132 and 816 diseased and non-diseased potato stem datasets. In the results of the potato leaf graph, the best results were obtained at epoch 25 with an accuracy value of testing data and training data of 82% and 81% with the loss model at epoch 25 being at a value of 0.42 for training data and 0.41 for testing data in the classification of diseased leaves. potato plant. The results of the classification of potato plant stems found the best value at epoch 25 with an accuracy value of 85% on testing data and 86% on training data. The model loss value in the training set is 0.34 and the validation test value is 0.33 at epoch 24
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Copyright (c) 2024 Abdul Rosid, Abd. Ghofur, Firman Santoso
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