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- W2155569884 abstract "Yang, C.-C., Prasher, S.O. and Landry, J.A. 2002. Weed recognition in corn fields using back-propagation neural network models. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 44: 7.15-7.22. The objective of this study was to develop backpropagation artificial neural network (ANN) models to distinguish young corn plants from weeds. Digital images were taken in the field under various natural lighting conditions. The images were cropped and resized to smaller sub-images containing either corn plants or weeds. The green objects in the images were extracted with the greenness method, thus counting the pixels with a green intensity larger than red and blue intensities and replacing other background pixels with the intensity of zero. The extracted colour images were then converted to intensity images to save computational efforts during the ANN model development. The number of images available for training was quadrupled by rotating counter-clockwise each image by 90, 180, and 270 degrees. Several hundred images of corn plants and weeds were used for training the model. The ability of the ANN models to discriminate weeds from corn was tested. The highest success recognition rate was for corn at 100%, followed respectively by Abutilon theophrasti at 92%, Chenopodium album at about 62% and Cyperus esculentus at 80%. The ANN model required less than one minute to process 250 images containing 80x80 pixels. The inability of ANN models to properly classify weeds and corn plants was due to our inability to use a greater number of processing elements in the formulation of the ANN models. This was most likely caused by issues related to PC memory management." @default.
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- W2155569884 date "2002-01-01" @default.
- W2155569884 modified "2023-09-28" @default.
- W2155569884 title "Weed recognition in corn fields using back-propagation neural network models" @default.
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