Healthy Fruits Image Label Categorization through Color Shape and Texture Features Based on Machine Learning Algorithm

Abstract

The fruit categorization according to their visual quality has recently experienced tremendous growth in the field of agriculture and food products. Due to post-harvest loses during handling and processing, there is an increasing demand for quality products in agro industry which requires accuracy to predict the fruit. Various techniques of machine learning have been successfully applied for classifying the fruit built on binary class. In this paper, machine leaning technique is used to automate the process of categorization and to improve the accuracy of different types of fruits by feature selection. To categorized images domain specific features such as color, shape and textual features are considered. Statistical color features are extracted from the image, bounding box feature for shape features and gray-level co-occurrence matrix (GLCM) is used to extract the textual feature of an image. These features are combined in a single feature fusion. A support vector machine (SVM) classification model is trained using training set features on fruit360 dataset which includes six fruit categories (classes) with two sub category (sub-classes) which builds multiclass classification task. We present one-vs-one coding design of Error correcting output codes (ECOC) and apply to SVM classifier; validation followed a fivefold cross validation strategy. The result shows that the textual features combined with color and shape feature improved fruit classification accuracy.

Utilization of Support Vector Machine and Speeded up Robust Features Extraction in Classifying Fruit Imagery

“…This is especially for the target category greater than two. The article [7] proposed the ECOC framework to transform Multiclass into several binary issues. SVM when combined with ECOC enriches the system of failure when completing a multi-screen classification [5].…”

“…The ECOC classification is used KFold. Langkah7: Predict the accuracy of classification for Test Data [7].…”

“…The optimization of the parameters can be used when the value/pattern has good quality. In a value/pattern that is arguably good, can be selected using the optimization of parameters, which is by using cross-validation [7].…”

“…The results show that the proposed technique has resulted in a better level of accuracy compared to existing methods. Also, the training time is less than that of the existing methods [7].…”

“…Also, SURF is very sensitive to changes in perspective (viewpoint) imagery, for example, changes such as left or right rotation or often referred to as planar changes [8]. SURF is the development of the Scale Invariant Feature Transform (SIFT) feature extraction algorithm [7]. Feature extraction is a method for generating traits from an image.…”