Abstract:
This study proposes an intelligent system for automated illness diagnosis and categorization in banana fruit, as well as an
integrated grading system. To accomplish accurate illness identification and grading, the suggested system incorporates computer
vision methods, machine learning algorithms, and deep learning models. The system extracts key information from banana fruit images
using image processing techniques, which are subsequently input into a trained classification model. The categorization model uses
cutting-edge algorithms to categorize the banana fruit into several illness groups. Furthermore, the sophisticated grading system
evaluates the severity and quality of the diseased fruit based on a variety of characteristics such as size, color, and texture. The
experimental findings reveal that the proposed method is successful, with high accuracy in illness diagnosis and accurate banana
grading. This automated technology provides a time-efficient and cost-effective approach for disease control in banana plantations,
allowing producers and agricultural stakeholders to make more informed decisions
