Identification of Rice Varieties Using Machine Learning Algorithms
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Date
2022
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Ankara Üniversitesi
Abstract
Rice, which has the highest production and consumption rates worldwide,
is among the main nutrients in terms of being economical and nutritious
in our country as well. Rice goes through some stages of production from
the field to the dinner tables. The cleaning phase is the separation of rice
from unwanted materials. During the classification phase, solid ones and
broken ones are separated and calibration operations are performed.
Finally, in the process of extraction based on color features, the striped
and stained ones other than the whiteness on the surface of the rice grain
are separated. In this paper, five different varieties of rice belonging to
the same trademark were selected to carry out classification operations
using morphological, shape and color features. A total of 75,000 rice grain
images, including 15,000 for each varieties, were obtained. The images
were pre-processed using MATLAB software and prepared for feature
extraction. Using a combination of 12 morphological, 4 shape features
and 90 color features obtained from five different color spaces, a total of
106 features were extracted from the images. For classification, models
were created with algorithms using machine learning techniques of knearest neighbor, decision tree, logistic regression, multilayer perceptron,
random forest and support vector machines. With these models,
performance measurement values were obtained for feature sets of 12, 16,
90 and 106. Among the models, the success of the algorithms with the
highest average classification accuracy was achieved 97.99% with
random forest for morphological features. 98.04% were obtained with
random forest for morphological and shape features. It was achieved with
logistic regression as 99.25% for color features. Finally, 99.91% was
obtained with multilayer perceptron for morphological, shape and color
features. When the results are examined, it is observed that with the
addition of each new feature, the success of classification increases.
Based on the performance measurement values obtained, it is possible to
say that the study achieved success in classifying rice varieties.
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Keywords
Color features