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ARS Home » Plains Area » Mandan, North Dakota » Northern Great Plains Research Laboratory » Research » Publications at this Location » Publication #299194
Title: Digital image processing based identification of nodes and internodes of chopped biomass stems

Author
item POTHULA, ANAND - North Dakota State University
item IGATHINATHANE, C - North Dakota State University
item Kronberg, Scott
item Hendrickson, John

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/10/2014
Publication Date: 5/8/2014
Publication URL: http://handle.nal.usda.gov/10113/59045
Citation: Pothula, A.K., Igathinathane, C., Kronberg, S.L., Hendrickson, J.R. 2014. Digital image processing based identification of nodes and internodes of chopped biomass stems. Computers and Electronics in Agriculture. 105:54-65.

Interpretive Summary: In respect to converting grass into biofuel, the chemical composition of various parts of grass plants differ and this has consequences for the efficiency of converting grass into biofuel. Even different parts of stem nodes and internodes vary considerably in chemical composition and if grass can be coarsely ground then stem nodes and internodes separated, conversion of these stem parts into biofuel can be more efficient. Therefore, this study evaluated the use of digital image analysis to identify and quantify grass stem nodes and internodes using stems of big bluestem, corn and switchgrass. Unit area under a normalized gray value curve was found to be very effective and the best parameter for identification of nodes and internodes for these grasses. This image processing methodology can be the supporting software for the hardware systems that perform the physical separation of node and internode portions of grass.

Technical Abstract: 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.