Classification of Date Fruits into Genetic Varieties Using Image Analysis

A great number of fruits are grown around the world, each of which has various types. The factors that determine the type of fruit are the external appearance features such as color, length, diameter, and shape. The external appearance of the fruits is a major determinant of the fruit type. Determin...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021-11, Vol.2021, p.1-13
Main Authors: Koklu, Murat, Kursun, Ramazan, Taspinar, Yavuz Selim, Cinar, Ilkay
Format: Article
Language:English
Subjects:
Agriculture
Artificial neural networks
Cameras
Classification
Color
Computer vision
Discriminant analysis
Feature extraction
Fruits
Image analysis
Image classification
Image processing
Learning theory
Machine learning
Mathematical problems
Morphology
Neural networks
Success
Support vector machines
Vision systems
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Summary:A great number of fruits are grown around the world, each of which has various types. The factors that determine the type of fruit are the external appearance features such as color, length, diameter, and shape. The external appearance of the fruits is a major determinant of the fruit type. Determining the variety of fruits by looking at their external appearance may necessitate expertise, which is time-consuming and requires great effort. The aim of this study is to classify the types of date fruit, that are, Barhee, Deglet Nour, Sukkary, Rotab Mozafati, Ruthana, Safawi, and Sagai by using three different machine learning methods. In accordance with this purpose, 898 images of seven different date fruit types were obtained via the computer vision system (CVS). Through image processing techniques, a total of 34 features, including morphological features, shape, and color, were extracted from these images. First, models were developed by using the logistic regression (LR) and artificial neural network (ANN) methods, which are among the machine learning methods. Performance results achieved with these methods are 91.0% and 92.2%, respectively. Then, with the stacking model created by combining these models, the performance result was increased to 92.8%. It has been concluded that machine learning methods can be applied successfully for the classification of date fruit types.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/4793293