Artificial Intelligence-Based Prediction of Some Pomegranate Diseases
DOI:
https://doi.org/10.24925/turjaf.v13i4.946-957.7373Keywords:
Deep Learning, Support Vector Machines, Convolutional Neural Networks, Machine Learning, Pomegranate Disease ClassificationAbstract
The early diagnosis and classification of plant diseases in the agricultural sector is of great importance for reducing crop losses and increasing productivity. Particularly, the pomegranate is a fruit with high economic value, and its diseases can seriously affect both quality and production. In this context, the use of machine learning and deep learning methods for the diagnosis and classification of various pomegranate diseases is being investigated. A total of 5099 pomegranate images collected from farms in the Karnataka state of India have been used. The images are classified into five categories: Early Leaf Burn, Black Spot Disease, Bacterial Blight, Leaf Spot Disease, and Healthy. The dataset has been classified using Support Vector Machines (SVM), Decision Trees (DT), and Convolutional Neural Networks (CNN). The findings show that CNN models provide higher accuracy rates in the detection of pomegranate diseases compared to other machine learning methods. Specifically, the CNN model with two convolutional layers exhibited the best performance with an accuracy rate of 88%. Decision Trees, on the other hand, had lower accuracy rates compared to other models. It has been demonstrated that deep learning and machine learning models can be effectively used for the diagnosis of plant diseases and can increase efficiency in the agricultural sector.
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