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ARS Home » Plains Area » Mandan, North Dakota » Northern Great Plains Research Laboratory » Research » Publications at this Location » Publication #305561
Title: Identification of nodes and internodes of chopped biomass stems by Image analysis

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

Submitted to: ASABE Annual International Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 1/9/2014
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Separating the morphological components of biomass leads to better handling, more efficient processing as well as value added product generation, as these components vary in their chemical composition and can be preferentially utilized. Nodes and internodes of biomass stems have distinct chemical compositions but are difficult to segregate by simple mechanical means, even though they are clearly different in visual appearance. Difference in appearance between nodes and internodes, especially color variation along the axial length, was used as an identification strategy in this image analysis study. Chopped stems of corn, switchgrass, and big bluestem were used as test material, and samples of nodes and internodes were prepared. Digital images of nodes and internodes were obtained using a digital scanner. Image processing algorithm development was performed in a MATLAB environment. A step-by-step procedure for identifying a narrow computational band (CB) along the major axis was developed. Information on this narrow CB was found sufficient for node and internode identification. Based on the image gray value within the CB, several features, such as minimum, maximum, average, standard deviation, and variation of CB gray values; ribbon length of CB normalized gray value curve (NGVC), unit ribbon length of NGVC; and area under NGVC, and unit area under NGVC were extracted. Unit area under the NGVC in the CB ranked highest with 99.9% node and internode identification accuracy, among the other tested features. The outlined algorithm can be used to develop electronic system hardware that can perform the actual sorting.