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Finding Color Blindness Using Ishihara Algorithm

Srinivasulu Munakala, Tejesh D., Sumathi P.


This paper is aimed to develop a back propagation artificial neural network (ANN) model that could distinguish crop plants from weeds. Although only the color indices associated with image pixels were used as inputs, it was assumed that the ANN model could develop the ability to use other information, such as shapes, implicit in these data. The 756x504 pixel images were taken in the field and were then cropped to 100x100-pixel images depicting only one plant, either a corn plant or weeds. There were 40 images of corn and 40 of weeds. The ability of the ANNs to discriminate weeds from corn was then tested on 20 other images. A total of 10 images of corn plants and weeds were used for training purposes. For some ANNs, the success rate for classifying corn plants was as high as 100%, whereas the highest success rate for weed recognition was 80%. This is considered satisfactory, given the limited amount of training data and the computer hardware limitations. Therefore, it is concluded that an ANN-based weed recognition system can potentially be used in the precision spraying of herbicides in agricultural fields.


Keywords: Dyschromatopsia, achromatopsia, blue cone monochromatism,
self-organising map (SOM)


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