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- W2061675551 abstract "Currently, inspection of wheat in the United States for grade and class is performed by human visual analysis. This is atime consuming operation typically taking several minutes for each sample. Digital imaging research has addressed thisissue over the past two decades, with success in recognition of differing wheat classes, and distinguishing wheat fromnon-wheat species. Detection of wheat kernel defects, either by damage or disease, has been a greater challenge. A studyhas been undertaken that uses high-speed black and white imaging at 10-bit photometric resolution to detect damagedkernels one kernel at a time. The system, composed of hardware (camera, lighting, power supplies, and data acquisitioncard), software (LabVIEW and MATLAB), and analytical (MATLAB and SAS) components, is designed to a) captureimages of free-falling kernels at opposing angles through the use of optical grade mirrors, b) parameterize the imagesand, c) perform classification. The system operates with a 1/30,000 second exposure time though with restrictions onimage transfer rate (60 Hz) and image processing routines for feature extraction (currently conducted offline). Fiftysamples of hard red and white wheat subjected to weather related damage during plant development were used in thisstudy. Parametric (linear discriminant analysis) and non-parametric (k-nearest neighbor) classification models weretested to determine the image features that best foster recognition of the damage conditions of mold, sprout, and blacktip. The morphological features used in classification included area, projected volume, perimeter, elliptical eccentricity,and major and minor axis lengths. Textural features from calculated gray level co-occurrence matrices (includingcontrast, correlation, energy, and homogeneity), are also under consideration though not reported herein. So far, ourresults indicate that with as few as three image parameters, classification (damaged vs. sound) levels approach 85 to 90percent accuracy. Information learned from this study is intended to lead to the streamlining of feature extraction inimage-based high speed sorting." @default.
- W2061675551 created "2016-06-24" @default.
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- W2061675551 date "2012-05-01" @default.
- W2061675551 modified "2023-10-02" @default.
- W2061675551 title "Development of a single-channel, three-view imaging system with classification model for defect and damage assessment of freefalling cereal grains" @default.
- W2061675551 doi "https://doi.org/10.1117/12.921419" @default.
- W2061675551 hasPublicationYear "2012" @default.
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