Classification of Peanut Images Based on Multi-features and SVM. Issue 17 (2018)

• Record Type: Journal Article
• Authors: Li, Zhenbo; Niu, Bingshan; Peng, Fang; Li, Guangyao; Yang, Zhaolu; Wu, Jing
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 17(2018)
• Page Start: 726
• Page End: 731
• Publication Date: 2018
• Subjects: image classification -- HOG -- Aspect ratio -- Hu invariant moment -- SVM; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.08.110
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This article provides a method for accurate classification of peanuts. Peanuts can be classified into three categories, including one peanut, two peanuts and three peanuts. Because different peanuts have different prices. The characteristics of peanut images were extracted by three different methods including the convolution neural network of aspect ratio, HOG and Hu invariant moment, and then classifying peanut images respectively by the SVM (support vector machine). The accuracy rate of the aspect ratio + SVM algorithm, HOG+SVM algorithm, Hu invariant moment +SVM algorithm respectively is 96.72%, 81.97% and 81.97%, realize the industrialization of peanut classification.