Rice, which is among the most widely produced grain products worldwide, has many genetic varieties. These varieties are separated from each other due to some of their features. These are usually features such as texture, shape, and color. With these features that distinguish rice varieties, it is possible to classify and evaluate the quality of seeds.
In this study, Arborio, Basmati, Ipsala, Jasmine and Karacadag, which are five different varieties of rice often grown in Turkey, were used. A total of 75,000 grain images, 15,000 from each of these varieties, are included in the dataset. A second dataset with 106 features including 12 morphological, 4 shape and 90 color features obtained from these images was used.
This notebook focuses on the rice image dataset on Kaggle, consisting of images of different rice varieties. The objective is to develop a machine learning model for accurate classification of rice images into their respective categories.
The goal is to build a robust machine learning model capable of accurately classifying rice images into their respective varieties. This can assist in various applications such as agricultural research, crop management, and quality control in the rice industry.
This script is open-source and licensed under the MIT License. For more details, check the LICENSE file.