Classification of Hazelnuts with CNN based Deep Learning System

Engin Gunes; Eyup Emre Ulku; Kazim Yildiz

Classification of Hazelnuts with CNN based Deep Learning System

Engin Gunes, Eyup Emre Ulku, Kazim Yildiz

Abstract

The rapid development of technology leads to the emergence of technology-based systems in many different areas. In recent years, agriculture has been one of these areas. We come across technological systems in agricultural applications for many different purposes such as growing healthier products, increasing the yield of products, and predicting product productivity. Today, technology-based systems are used more and more widely in agricultural applications. Classification of products quickly and with high accuracy is a very important process in predicting product yield. In this study, it is suggested to use the CNN-based deep learning model in order to classify the hazelnut fruit, which is an important agricultural product.

Using large-sized data sets has an important role in achieving high performance in deep learning-based systems. In this direction, a data set consisting of more than 15 thousand hazelnut images was created and presented to the use of researchers.

In this study, the classification of hazelnut images was carried out using the VGG16 deep learning model, which is a powerful model for classifying images. As a result of the experiments on the data set created, the classification process of hazelnuts was realized with 98% f1 score. In addition, in the study, in order to show the importance of the size of the dataset used in the deep learning approach, the classification process was carried out with the same model by using 50%, 25% and 10% of the data set. It was observed that the 98% success achieved when the whole data set was used decreased to 95% when 50% was used, 93% when 25% was used, and 88% when 10% was used.

Keywords

CNN model, Deep learning, Classification, Hazelnuts dataset, Hazelnuts classification

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