Fruit recognition using Statistical and Features extraction by PCA

Abstract

A diet high in fruit can help us avoid diseases such as cancer, diabetes, heart disease, and others. Without professional dietitian guidance, a method that quickly reveals how many calories in fruits they are consuming can be helpful in maintaining health. Image processing methods is used to expanding across all academic fields, including food science and agriculture. The identification of plant fruits and the extraction of their features are covered the first topics in this essay because they are essential to agriculture. The main goal is to use the results of Principal Component Analysis (PCA) to build an accurate, efficient, and reliable framework. Fruit detecting software could simplify human labor. Based on color and shape characteristics, several fruit recognition methods have been developed. However, the color and shape values of many kinds of fruit photos could be comparable or even the same. As a result, utilizing PCA features extraction analysis methods to identify and distinguish fruit photos is still workweek and effective enough to boost recognition accuracy. In this paper, a fruit recognition algorithm based on PCA is proposed. The database that has been used in this study contains six distinct categories and 36 fruit images. Despite the fact that this approach used PCA for feature extraction to implement the system in this research paper, the proposed system's classification accuracy is reached around to 75%.