Banana Freshness Identification Using Image Processing Techniques

Abstract

Bananas provide rapid energy and are a worldwide available fruit. Bananas are also available all year and seldom cause health problems. Banana is one of the most significant fruits in Sri Lanka since it is extensively consumed and suitable for all situations. Bananas are undoubtedly healthful and have export worth. As a result, determining freshness is critical to ensuring product quality and market value. The conventional method for measuring the freshness of a banana in terms of days necessitates the naked eye inspection of experienced specialists. Because specialists are not always available, we developed a method for determining the freshness of bananas using image processing techniques. For this investigation, images of bananas at various levels were obtained using a high-quality mobile camera. K-Means clustering was used to identify the interesting region of the bananas, and a Support Vector Machine (SVM) model was utilized to estimate freshness by training using the chosen features from the input images. Several feature combinations were investigated for this study’s evaluation, and the relationship between the features Energy, Contrast, Correlation, RMS, Homogeneity, Mean, Standard deviation, Entropy, Greenness, Kurtosis, Skewness, and Variance yielded an accuracy of 81.75 percent. This system’s goal is to inspire future researchers to turn this method into a mobile application that also incorporates Artificial Intelligence (AI) for autonomous bulk observation.