Rice Classification and Quality Detection Success with Artificial Intelligence Technologies
Published in Computational Sciences
In this study, the success situation in the classification of rice by qualities with information technologies systems was aimed. In the study, the feature selection process was applied by making statistical analyzes of the features obtained from the images of two different rice species. The classification process was carried out with five different Artificial Intelligence (AI) algorithms using 6 different morphological features. When the results and performance values are examined, it was viewed that the Support Vector Machine (SVM) algorithm gave the highest accuracy in classification with 93.53%. The obtained Area Under the Curve (AUC) values showed that a very high classification result of 99.18% was accomplished. It was detected that morphological features were very important parameters in classifying rice varieties with the AI algorithms. It is accepted that this study will be important in accelerating the process of product classification which is one of the main components of agricultural marketing and classifying correctly crops.
Dr. Gökalp Çınarer is an Assistant Professor in the Department of Computer Engineering at Yozgat Bozok University. He completed his PhD in Computer Engineering at Kırıkkale University between 2017-2021 and wrote his thesis on "Detection of Brain Tumors with Image Processing Techniques and Analysis with Classification Algorithms".
His research interests focus on artificial intelligence, machine learning, image processing and deep neural networks. He has carried out important academic studies using artificial intelligence applications in different fields such as medicine, agriculture, food technologies and environmental analysis.
Since 2021, he has been continuing his administrative duties as the Head of the Department of Information Processing and Head of the Department of Computer Engineering Software at Yozgat Bozok University. He teaches undergraduate and graduate courses such as Machine Learning, Deep Learning, Python Programming, Algorithm Analysis, Artificial Intelligence Applications.
His academic publications include more than 75 articles scanned by SCI-Expanded, SSCI and Scopus, and international conference proceedings. His work covers various topics such as brain tumor analysis with artificial intelligence, artificial intelligence applications in agriculture, environmental data analysis and object detection.
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