Enhanced Classification Approaches for Varieties of Sweet Corn: Employing CNN and XGBoost Models for Madhuri, Sugar 75, and Sweet Gold

This study aims to develop a model, which will be based on Machine Learning (ML) techniques and will show great accuracy in identifying the different kinds of sweet corn, such as Madhuri, Sugar 75, and Sweet Gold. In collaboration with the CNN and the XGBoost algorithms, this study will enhance the model to be reliable. A comprehensive database is developed, containing high-quality images and features that are important for analysis. The preprocessing step includes the identification and removal of invalid data. The model performances are therefore quantified by using metrics such as accuracy, accuracy, precision, recall, and F1-score. Comparative study showed through tests and analysis that these two AI algorithms can sort sweet corn varieties with precision, which could increase agricultural development and the market segmentation in India.