80.7%。Characteristics of the Aphis gossypii area, background and mixed area (include Aphis gossypii area and background) were analyzed and principle of determining was establish based on the threshold G component. Then Aphis gossypii area and non-Aphis gossypii area were separated using the threshold G component. For the overlapping Aphis gossypii, the input image were marked using the minimum extension transform, then distance transform and watershed algorithm was applied to the marked image, and the overlapping was removed. Experimental results showed that this algorithm could effectively segment the overlapping Aphis gossypii. The sum of over-segmentation rate and under-segmentation rate was 3.14%. The accurate rate was 96.2%, which was higher than the direct counting.
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邱白晶,王天波,李娟娟,李坤.黄瓜蚜虫的图像识别与计数方法[J].农业机械学报,2010,41(8):151-155.Image Recognition and Counting for Glasshouse Aphis gossypii[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(8):151-155.