{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T06:17:56Z","timestamp":1774160276220,"version":"3.50.1"},"reference-count":22,"publisher":"IGI Global","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,7]]},"abstract":"<jats:p>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%.<\/jats:p>","DOI":"10.4018\/ijitwe.2017070101","type":"journal-article","created":{"date-parts":[[2017,5,19]],"date-time":"2017-05-19T13:09:01Z","timestamp":1495199341000},"page":"1-10","source":"Crossref","is-referenced-by-count":8,"title":["Image Recognition of Rapeseed Pests Based on Random Forest Classifier"],"prefix":"10.4018","volume":"12","author":[{"given":"Li","family":"Zhu","sequence":"first","affiliation":[{"name":"Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, China"}]},{"given":"Minghu","family":"Wu","sequence":"additional","affiliation":[{"name":"Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, China"}]},{"given":"Xiangkui","family":"Wan","sequence":"additional","affiliation":[{"name":"Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, China"}]},{"given":"Nan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, China"}]},{"given":"Wei","family":"Xiong","sequence":"additional","affiliation":[{"name":"Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, China"}]}],"member":"2432","reference":[{"key":"IJITWE.2017070101-0","doi-asserted-by":"publisher","DOI":"10.1049\/ip-vis:20000630"},{"key":"IJITWE.2017070101-1","unstructured":"Chen, Z.B., & Yu, J. (2010). Research strategies based on the analysis of rapeseed production in China. Chinese journal of oil crop sciences, 32(2), 303-308."},{"key":"IJITWE.2017070101-2","unstructured":"Zhang, J., & Wang, R., Xie, C., & Li, R. (2014). Crop Pests Image Recognition Based on Multi-features Fusion.Journal of Computer Information Systems, 10(12), 5121\u20135129."},{"key":"IJITWE.2017070101-3","first-page":"440","article-title":"Dong, A., & Song,","volume":"43","year":"2013","journal-title":"Journal of Jilin University"},{"key":"IJITWE.2017070101-4","unstructured":"Qiao, H., Xia, B., Ma, X., Cheng, D., & Zhou, Y. (2010). Identification of Damage by Diseases and Insect Pests in Winter Wheat.Journal of Triticeae Crops, 30(4), 770\u2013774."},{"key":"IJITWE.2017070101-5","unstructured":"Huang, X., Lin, D., Zhu, S., & Zhao, J. (2008). Study on identification of beetle insect using computer vision.Microcomputer Information, 24(3), 303\u2013305."},{"issue":"8","key":"IJITWE.2017070101-6","first-page":"178","article-title":"Recognition of Pest Damage for Cotton Leaf Based on RBF - SVM Algorithm.","volume":"42","author":"Z.Jianhua","year":"2011","journal-title":"Transactions of the Chinese Society for Agricultural Machinery"},{"key":"IJITWE.2017070101-7","doi-asserted-by":"crossref","unstructured":"Shivadas, J.M., & Gauch, A. (2007). Real-Time Commercial Recognition Using Color Moments and Hashing. Proceedings of theFourth Canadian Conference on Computer and Robot Vision.","DOI":"10.1109\/CRV.2007.53"},{"issue":"20","key":"IJITWE.2017070101-8","first-page":"244","article-title":"The Research Progress on Machine Recognition of Plant Diseases and Insect Pests.","volume":"31","author":"C.Leping","year":"2015","journal-title":"Chinese Agricultural Science Bulletin"},{"key":"IJITWE.2017070101-9","unstructured":"Yuan Lin, Zhang Jingcheng, Wang Jihua, \u201cResearch progress of crop diseases and pests monitoring based on remote sensing.\u201d ransactions of the Chinese Society of Agricultural Engineering, 2012, 28(20): pp. 1-11."},{"issue":"12","key":"IJITWE.2017070101-10","first-page":"186","article-title":"A plant pests and diseases detection method based on multi-features fusion and svm classifier.","volume":"31","author":"J.Longquan","year":"2014","journal-title":"Computer Applications and Software"},{"key":"IJITWE.2017070101-11","doi-asserted-by":"publisher","DOI":"10.1038\/467154a"},{"issue":"1","key":"IJITWE.2017070101-12","first-page":"176","article-title":"Research progress and prospect of technology spillover effect of industrial transfer","volume":"37","author":"S.Pan","year":"2015","journal-title":"Progress in Geography"},{"key":"IJITWE.2017070101-13","doi-asserted-by":"publisher","DOI":"10.5120\/20796-3457"},{"issue":"7","key":"IJITWE.2017070101-14","first-page":"1870","article-title":"Identification of vegetable leaf-eating pests based on image analysis.","volume":"37","author":"C. 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