Open AccessAn Approach for Detecting Fruit Quality with RBF-SVM Classification Model
Author(s) -
Sonia Chaudhary
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.36426
Subject(s) - computer science , support vector machine , scheme (mathematics) , artificial intelligence , machine learning , matlab , fuzzy logic , orange (colour) , data mining , pattern recognition (psychology) , mathematics , mathematical analysis , horticulture , biology , operating system
For the regular development of rural area especially for the agriculture domain, automation is very important. Currently fruit's quality plays an important factor in their sales and production. Detecting the quality of fruits by using manual methods are not recommended because of the reasons that it cause delay and the results are also not upto the mark. Therefore, machine learning and computer vision is gaining much interest from current researchers to develop fruit quality detection systems. This paper contributes to provide an effective and advanced Orange fruit quality detection system. the proposed scheme is focused on giving an Fuzzy C-Mean based region of interest extracting scheme along with RBF-SVM classification model to improve the classification rate in comparison to existing approaches. The proposed scheme is simulated in MATLAB software and results are evaluated in terms of various performance factors such as Accuracy, Sensitivity, Specificity, Precision, Recall and F-Score. Finally a comparison of the proposed scheme is given that show an improvement of approximately 18% with respect to various state of art techniques. this strengthen the recommendation of proposed scheme for future fruit quality analysis system.