Analysis of Potato Diseases Using Image Processing Methods for Detection
DOI:
https://doi.org/10.58190/ijamec.2026.170Keywords:
Potato diseases, Image processing, Machine learning, Performance analysisAbstract
Potatoes are one of the basic food sources consumed by living organisms. Potatoes are naturally vulnerable to diseases. Potatoes are a food that can spoil over time, and their nutritional value gradually decreases during this process. Therefore, the quality of potatoes needs to be continuously monitored during the production phase. For this reason, image processing tools are now being used to detect potato diseases. Based on this motivation, this study uses data from 451 different potato images. This dataset contains 7 disease classes: Black Scurf, Blackleg, Common Scab, Dry Rot, Healthy Potatoes, Miscellaneous, and Pink Rot. Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR) methods were used for the classification of this data. Confusion matrix and accuracy, precision, recall, and F1 Score metrics were used to analyze the classification success of the models. As a result of training and testing the models, classification success rates of 68.5% were achieved with the ANN model, 54.8% with the KNN model, 65.6% with the SVM model, and 67.0% with the LR model. The highest classification success was obtained from the ANN model. In conclusion, it can be said that all classification models can be used to detect potato diseases.
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