Abstract

This paper describes a method for classifying the quality of Kashmiri apples using a machine-learning algorithm. Machine learning (ML) is applied to model the system for analyzing the quality of Kashmiri delicious apples. The Kashmiri apple samples were utilized as input for the algorithm based on their images. Collecting as many samples as possible to maximize accuracy is a recommended method for organizing and managing the system. The database would then be constructed to be used for classifying the quality of Kashmiri apples. The categorization mechanism in the work is configured to provide appropriate accuracy. This paper suggests an Apple classification strategy based on support vector machine (SVM) considering the inefficiency and poor accuracy of current classification technologies. The image of the Kashmiri delicious apples is recognized throughout the communication process, and artificial intelligence technology is used for the sorting process. First, the goal area of the Apple is retrieved utilizing morphological techniques, hole filling, and noise reduction techniques like median filtering. The Canny approach is then used to obtain the binary picture, other features are also extracted like the apple fruit shape, size, color, and fault features. Finally, the SVM is optimized using the genetic algorithm, which is also used to build and train the classifier model and choose the kind of test sample. The trained model results show that the support vector machine improved is used in sorting Kashmiri delicious apples into different classes based on their quality.

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