Identification of soluble solid content and total acid content using real-time visual inspection system

C. H. V. K. N. S. N. Moorthy, Mukesh Kumar Tripathi, Manjunath R. Hudagi, Lingaraj A. Hadimani, Gayatri Sanjay Chavan, Sanjeevkumar Angadi


This paper presents the framework for identifying materials using a fused descriptor-based approach, leverage computer vision techniques. The system is structured into three phases: derivation, extraction, and portrayal. Initially, the system employs K-means gathering techniques for establishing derivation. Following derivation, the system utilizes variety, texture, and shape-based feature extraction methods to extract relevant features from the soluble solid content and total acid content using real-time visual inspection system. A “consolidating” fusion feature is explored in the final phase using classification algorithms like C4.5, support vector machines (SVM), and k-nearest neighbors (KNN). The performance evaluation of the recognition system demonstrates promising results, with accuracy rates of 97.89%, 94.60%, and 90.25% achieved by using C4.5, SVM, and KNN separately. This indicates that the proposed fusion strategy effectively supports accurately recognizing materials using a fused descriptor-based approach.


Classifiers; Feature descriptor; Fruits; Recognition; Veggies

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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