Bulgarian Journal of Agricultural Science
Array ( [session_started] => 1634871673 [LANGUAGE] => EN [LEPTON_SESSION] => 1 )
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CHOOSING CLASSIFIER FOR WEED IDENTIFICATION IN SUGARCANE FIELDS THROUGH IMAGES TAKEN BY UAV
Inacio Henrique Yano, Wesley Esdras Santiago, Jose Ricardo Alves, Lia Toledo Moreira Mota, Barbara Teruel
Abstract: Sugarcane is the main raw material in the world production of sugar and ethanol. The weeds can cause 90% loss in sugarcane production. Thus the weed control is very important and usually made by herbicides application. The estimation of
herbicide type and its dosage is in general done by sampling because sugarcane occupies extensive areas. This procedure causes problems of misapplication of herbicide, since the weed species and the level of its infestations could not be uniform in whole field. There are some solutions based on remote sense, using satellite image analysis which covers the whole field, that could solve the problems of the applications of herbicides by sampling, but this solution have problems with image resolution, and can only be used on high weed infestation and in the absence of clouds for good results. This work proposed and tested a
process for weed surveying, based on pattern recognition in images taken by an UAV (Unmanned Aerial Vehicle). The UAV can take images very close to the plants, so the plants pattern recognition can be done in lower infestation levels than in images taken by satellites and also is not affected by the presence of clouds. In preliminary testes, three classifiers were tested; the best classifier was an Artificial Neural Network, which achieved an overall accuracy of 91.67% and a kappa coefficient of 0.8958.
Keywords: images; machine learning; pattern recognition; sugarcane; UAV; weed
Date published: 2017-07-07
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