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ARS Home » Plains Area » Manhattan, Kansas » Center for Grain and Animal Health Research » Stored Product Insect and Engineering Research » Research » Publications at this Location » Publication #286475
Title: Automated detection of insect-damaged sunflower seeds by X-ray imaging

Author
item Pearson, Thomas
item Prasifka, Jarrad
item Brabec, Daniel - Dan
item Haff, Ronald - Ron
item Hulke, Brent

Submitted to: Applied Engineering in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/6/2013
Publication Date: 1/1/2014
Citation: Pearson, T.C., Prasifka, J.R., Brabec, D.L., Haff, R.P., Hulke, B.S. 2014. Automated detection of insect-damaged sunflower seeds by X-ray imaging. Applied Engineering in Agriculture. 30(1):125-131.

Interpretive Summary: Breeding efforts to develop insect-resistant sunflowers is hindered by the lack of a quick and effective method for scoring samples in terms of insect damage. The current method for scoring insect damage is tedious and inconsistent as it involves manual inspection of seeds for holes bored into the shell. In this study, a method was developed to quickly place sunflower seeds in a closely packed grid where the seeds were consistently oriented. Subsequently, the grid of seeds was digitally x-ray imaged. A computer program was developed to analyze the images and classify each seed as damaged or undamaged. This computer program uses a simple but novel method for detecting seeds having asymmetrical morphology due to insect feeding. An overall classification accuracy for damaged and undamaged seeds was 95% and 99%, respectively. The method, including placing the seeds into the grid, imaging, and analyzing takes approximately 3 minutes per sample, and should be consistent over time. The method should aid in scoring sunflower seed varieties for insect resistance and could also be applied to other applications, such as detecting broken seeds.

Technical Abstract: The development of insect-resistant sunflowers is hindered by the lack of a quick and effective method for scoring samples in terms of insect damage. The current method for scoring insect damage, which involves manual inspection of seeds for holes bored into the shell, is tedious, requiring approximately 10 minutes per 100-kernel sample. In this study, a method was developed to quickly position 72 to 144 sunflower seeds in a grid of closely packed, non-touching seeds consistently oriented for X-ray imaging. A computer program was developed to analyze the images and classify each seed as damaged or undamaged. Applying the program to 20 different samples comprising 11 sunflower lines and infestation by three different species of insects resulted in an overall classification accuracy for damaged and undamaged seeds of 95% and 99%, respectively. The method takes approximately 3 minutes per sample. The detection algorithm uses a simple but novel method for detecting seeds having asymmetrical morphology due to insect feeding. The method should aid in scoring sunflower seed varieties for insect resistance and could also be applied to other applications, such as detecting broken seeds.