AI-Driven Grading of Mango Quality Using External Characteristics: A Machine Learning Approach

Right now, a lot of subjective human judgment goes into classifying mangoes and other fruits, especially when it comes to poor productivity, which results in less than ideal classification accuracy. Our work suggests a novel method for assessing fruit quality that takes into account both weight and exterior features in order to address this. Mangoes are graded into three groups, G1, G2, and G3, using machine learning approaches, namely Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). These models make use of a variety of input features, transforming unstructured visual data into structured datasets for study. After preparation, the data are tested and trained to provide reliable classification results.