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Digital image processing based identification of nodes and internodes of chopped biomass stems

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Chemical composition of biomass feedstock is an important parameter for optimizing the yield and economics of various bioconversion pathways. Although understandably, the chemical composition of biomass varies among species, varieties, and plant components, there is distinct variation even among stem components, such as nodes and internodes. Separation of morphological components that possess different quality attributes and utilizing them in “segregated processing” leads to better handling, efficient processing, and high-valued products generation. Separation of morphological components such as node and internodes of biomass stem that have closely related physical properties (e.g. size, shape, density, etc.) using equipment is highly difficult. However, as the nodes and internodes are clearly distinct in appearance by visual observation, the potential of digital image analysis for node and internode identification and quantification was investigated. Test materials used were chopped stems of big bluestem, corn, and switchgrass. Pixel color variation along the length was used as the principle of identifying the nodes and internodes. An algorithm in MATLAB was developed to evaluate the gray value intensity within a narrow computational band along the major axis of nodes and internodes. Several extracted image features, such as minimum, maximum, average, standard deviation, and variation of the computational band gray values; ribbon length of the computational band normalized gray value curve (NGVC), unit ribbon length of NGVC; area under NGVC, and unit area under NGVC were tested for the identification. Unit area under NGVC was found as the best feature/parameter for the identification of the nodes and internodes, which produced an accuracy of about 99.9% (9 incorrect out of 317 objects). This image processing methodology of nodes and internodes identification can be the supporting software for the hardware systems that perform the separation.
Anand Kumar Pothula , C. Igathinathane , S. Kronberg , J. Hendrickson
USDA Scientist Submission
ARS USDA Submissions 2014 v.105
Elsevier B.V.
Journal Articles, USDA Authors, Peer-Reviewed
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