Grading The Quality of Apple Fruits Using Machine Learning Technique


Authors : Rachana M Shet; Priyadarshini V; HOD harshavardhan Tiwari; Dr. Harshavardhan Tiwari

Volume/Issue : Volume 7 - 2022, Issue 6 - June

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3NXH011

DOI : https://doi.org/10.5281/zenodo.6816677

Abstract : This project introduces fruit quality identification system. The system design considers certain features including the color and shape of the fruit, which increases the accuracy for pixel detection of the fruit, using a color classification support vector machine (SVM). Image processing provides solutions for automated fruit size grading to provide accurate, reliable, consistent and quantitative information in addition to handling large quantities. Computer vision is a consistent and advanced technology for image processing with favorable results and immense potential. A computer vision has been strongly adopted in the heterogeneous sector including agriculture. Fruit grading is a very important function as it gives higher value to the grower and improves packaging, management and overdevelopment in the marketing system.

Keywords : GPU P100, GEFORCE10, Tensor flow, MATPLOT LIB, keras

This project introduces fruit quality identification system. The system design considers certain features including the color and shape of the fruit, which increases the accuracy for pixel detection of the fruit, using a color classification support vector machine (SVM). Image processing provides solutions for automated fruit size grading to provide accurate, reliable, consistent and quantitative information in addition to handling large quantities. Computer vision is a consistent and advanced technology for image processing with favorable results and immense potential. A computer vision has been strongly adopted in the heterogeneous sector including agriculture. Fruit grading is a very important function as it gives higher value to the grower and improves packaging, management and overdevelopment in the marketing system.

Keywords : GPU P100, GEFORCE10, Tensor flow, MATPLOT LIB, keras

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