Application of gaussian filter and extraction features for quality control of fruit raw materials in the puree industry

Abstract

The purpose of research in using Android-based raw material quality control applications in fruit puree factories is to enhance the industry's precision, uniformity, and efficiency. This application is developed to implement digital image processing utilizing two distinct methods: Gaussian filters and feature extraction. The implemented application captures guava data using the camera of an Android phone and subsequently resizes the image to dimensions of 600x600 pixels. Subsequently, the image colors are recovered by employing a Gaussian filter that operates on normalized Red-Green-Blue values. A minimum of 120 photos of red guava fruit from the raw material of one of the puree companies were subjected to image processing procedures. The application solely focuses on the color and texture of the fruit's skin, ensuring that the sample remains undamaged while adhering to hygienic guidelines. The ripeness degree of guava fruit is determined by employing an image classification algorithm with the K-nearest neighbor method. The application validation using K-fold cross-validation achieved an accuracy of 90.0% and a precision of 90.27% when applied to color imagery. When feature extraction was used, the accuracy was 83.3%, with a precision of 83.4%. Color extraction provides a more precise method for identifying ripe guava. The utilization of guava fruit ripeness detection in the quality control of raw materials for the puree sector has been simplified and made more user-friendly through the development of an Android-based application. Officers are not obligated to possess specialized expertise regarding the quality of raw materials.