Welcome to the InfoSci Platform
Reference Hub4
Image Recognition of Rapeseed Pests Based on Random Forest Classifier

Image Recognition of Rapeseed Pests Based on Random Forest Classifier

Li Zhu, Minghu Wu, Xiangkui Wan, Nan Zhao, Wei Xiong
Copyright: © 2017 |Volume: 12 |Issue: 3 |Pages: 10
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781522511632|DOI: 10.4018/IJITWE.2017070101
Cite Article Cite Article

MLA

Zhu, Li, et al. "Image Recognition of Rapeseed Pests Based on Random Forest Classifier." IJITWE vol.12, no.3 2017: pp.1-10. http://doi.org/10.4018/IJITWE.2017070101

APA

Zhu, L., Wu, M., Wan, X., Zhao, N., & Xiong, W. (2017). Image Recognition of Rapeseed Pests Based on Random Forest Classifier. International Journal of Information Technology and Web Engineering (IJITWE), 12(3), 1-10. http://doi.org/10.4018/IJITWE.2017070101

Chicago

Zhu, Li, et al. "Image Recognition of Rapeseed Pests Based on Random Forest Classifier," International Journal of Information Technology and Web Engineering (IJITWE) 12, no.3: 1-10. http://doi.org/10.4018/IJITWE.2017070101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Rapeseed pests will result in a rapeseed production reduction. The accurate identification of rapeseed pests is the foundation for the optimal opportunity for treatment and the use of pesticide pertinently. Manual recognition is labour-intensive and strong subjective. This paper propsed a image recognition method of rapeseed pests based on the color characteristics. The GrabCut algorithm is adopted to segment the foreground from the image of the pets. The noise with small area is filtered out. The benchmark images is obtained from the minimum enclosing rectangle of the rapeseed pests. Two types of color feature description of pests is adopt, one is the three order color moments of the normalized H/S channel; the other is the cross matching index calculated by the reverse projection of the color histogram. A multi-dimensional vector, which is used to train the random forest classifier, is extracted from the color feature of the benchmark image. The recognition results can be obtained by inputing the color features of the image to be detected to the random forest classifier and training.The experiment showed that the proposed method may identify five kinds of rapeseed accurately such as erythema, cabbage caterpillar, colaphellus bowringii baly, flea beetle and aphid with the recognition rate of 96%.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.