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    Author(s): D. Earl Kline; Chris Surak; Philip A. Araman
    Date: 2003
    Source: Computers and Electronics in Agriculture. 41: 139-155.
    Publication Series: Miscellaneous Publication
    PDF: Download Publication  (569.95 KB)


    Over the last 10 years, scientists at the Thomas M. Brooks Forest Products Center, the Bradley Department of Electrical and Computer Engineering, and the USDA Forest Service have been working on lumber scanning systems that can accurately locate and identify defects in hardwood lumber. Current R&D efforts are targeted toward developing automated lumber grading technologies. The objective of this work is to evaluate hardwood lumber grading accuracy based on current state-of-the-art multiple sensor scanning technology, which uses laser profile detectors, color cameras, and an X-ray scanner. Eighty-nine red oak boards were scanned and graded using Virginia Tech’s multiple sensor scanning system. The same boards were also manually graded on a normal production line. Precise board grade was determined by manually digitizing the boards for actual board defects. A certified National Hardwood Lumber Association (NHLA) employed lumber inspector then graded the lumber to establish a certified market value of the lumber. The lumber grading system was found to be 63% accurate in classifying board grade on a board-by-board basis. While this accuracy may seem low, the automated lumber grading system was found to be 31% more accurate than the line graders, which were found to be 48% accurate. Further, the automated lumber grading system estimated lumber value to within less than 6% of the NHLA certified value, whereas the line grader overestimated the lumber value by close to 20%. Most automated lumber grading discrepancies resulted from board geometry related issues (e.g. board crook, surface measure rounding, calculation of cutting units, etc.). Concerning the multiple sensor scanning system, defect recognition improvements should focus on better methods to differentiate surface discoloration from critical grading defects. These results will help guide the development of future scanning hardware and image processing software to more accurately identify lumber grading features.

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    Kline, D. Earl; Surak, Chris; Araman, Philip A. 2003. Automated hardwood lumber grading utilizing a multiple sensor machine vision technology. Computers and Electronics in Agriculture. 41: 139-155.

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