Author
Listed:
- Li Zhu
(Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, China)
- Minghu Wu
(Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, China)
- Xiangkui Wan
(Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, China)
- Nan Zhao
(Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, China)
- Wei Xiong
(Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, China)
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%.
Suggested Citation
Li Zhu & Minghu Wu & Xiangkui Wan & Nan Zhao & Wei Xiong, 2017.
"Image Recognition of Rapeseed Pests Based on Random Forest Classifier,"
International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 12(3), pages 1-10, July.
Handle:
RePEc:igg:jitwe0:v:12:y:2017:i:3:p:1-10
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jitwe0:v:12:y:2017:i:3:p:1-10. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.