ABSTRACT
The challenge of identifying and categorizing fruit illnesses is extremely complex because different fruit varieties can have noticeable differences in their features. We use a feature vector technique to handle these difficulties since we know it works well for managing this kind of variability. Every fruit goes through picture pre-processing to improve its representation before features are extracted. Together with the fruit samples, we establish a classified training dataset that we use to build a Convolutional Neural Network (CNN) model sorting fruits into fresh and rotting categories is the main goal. Important phases of the suggested model's development include feature creation, classifier learning, and pre-processing.
