Machine Learning-Based Classification of Mulberry Leaf Diseases




Computer Vision, Image classification, Machine Learning, Mulberry leaf image, Plant Disease, SVM.


This research examines the potential of machine learning methods in the classification of Mulberry leaf diseases. By applying SqueezeNet's deep feature extraction, the study aimed to identify disease patterns efficiently. The dataset used in the study consisted of ten distinct classes of Mulberry leaf diseases, which was divided into an 80% training set and a 20% testing set. The Support Vector Machine (SVM) supervised machine learning algorithm was used to classify the diseases, and the classification model achieved an accuracy of 77.5%. The results of the study demonstrate the effectiveness of machine learning approaches in aiding the detection and management of Mulberry leaf diseases, which can contribute to advancements in agricultural disease monitoring and mitigation strategies.

Author Biography

Murat KOKLU, Selcuk University

Murat KOKLU was born in Konya, TURKEY in 1979. He received B.Sc. degrees in Computer System Teaching in 2002 and Computer Engineering in 2019 from the Selcuk University. He received M.Sc. and Ph.D degrees from Selcuk University, Departments of Electronics and Computer Sciences, and Computer Engineering, in 2005 and 2014 respectively. He has been working as an Assistant Professor in the Department of Computer Engineering at Selcuk University. His current research interests include image processing, data mining and artificial intelligence.




How to Cite

YASIN , E. T., KURSUN, R., & KOKLU, M. (2024). Machine Learning-Based Classification of Mulberry Leaf Diseases. Proceedings of International Conference on Intelligent Systems and New Applications, 2, 58–63.