Digital image processing based identification of nodes and internodes of chopped biomass stems Anand Kumar Pothula a , C. Igathinathane a,⇑ , S. Kronberg b , J. Hendrickson b a Department of Agricultural and Biosystems Engineering, North Dakota State University, 1221 Albrecht Boulevard, Fargo, ND 58102, USA b Northern Great Plains Research Laboratory, USDA-ARS, 1701 10th Avenue SW, Mandan, ND 58554, USA article info Article history: Received 27 September 2013 Received in revised form 12 February 2014 Accepted 10 April 2014 Keywords: Algorithm development Biomass processing Grading and sorting Gray value Machine vision MATLAB abstract Chemical composition of biomass feedstock is an important parameter for optimizing the yield and economics of various bioconversion pathways. Although 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 possessing different quality attributes and utilizing them in ‘segregated processing’ leads to better handling, more efficient processing, and high-valued products generation. Using equipment to separate morphological components such as node and internodes of biomass stem that have closely related physical properties (e.g., size, shape, density) is 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 inves- tigated. We used chopped stems of big bluestem, corn, and switchgrass as test materials. Pixel color var- iation 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 the best feature/ parameter for the identification of the nodes and internodes with an accuracy of about 96.6% (9 incorrect out of 263 objects). This image processing methodology of nodes and internodes identification can form the supporting software for the hardware systems that perform the separation. Published by Elsevier B.V. 1. Introduction In recent years biomass feedstock has become a potential source of various renewable energy, fuel, and product applications due to its environmental benefits and local availability. Biomass feedstock can be either converted into gaseous or liquid biofuels in biochemical conversion or can be used directly in thermo chem- ical conversion (e.g., combustion; McKendry (2002)). Optimum energy conversion depends on the relative proportions of chemical composition (cellulose, hemicellulose and lignin) of biomass (McKendry, 2002). Other researchers (Sluiter et al., 2010; Ye et al., 2008) also reported the conversion yield from a biochemical process and process economics standpoint were determined by the accurate analysis of chemical composition of biomass feedstock. Chemical composition of biomass feedstock varies with plant variety, location, harvest and storage time (Hames et al., 2003). Liu et al. (2010) reported that morphological components, such as nodes, internodes, leaves, and pith contribute to chemical composition of biomass feedstocks. They also reported that the switchgrass internode contains high glucan content than nodes, thus internodes are more suited for ethanol production. Jung and Vogel (1992) analyzed leaf and stem fractions of switchgrass and big bluestem and found the best predictors for fiber digestibility differed among species, plant parts, and maturity and leaves contained less lignin than stems. Hu et al. (2010) found that the chemical and structural analytical results (heat combustion value, extractive contents, and chemical compositions) among the morphological components of switchgrass (nodes, internodes, and leaves) were significantly different. They reported that lignin and glucose content of switchgrass differed by 3.4% and 8.7%, respectively, among the node, internode and leaves. Ye et al. (2008) studied the chemical composition of corn using Fourier http://dx.doi.org/10.1016/j.compag.2014.04.006 0168-1699/Published by Elsevier B.V. ⇑ Corresponding author. Tel.: +1 701 667 3011; fax: +1 701 667 3054. E-mail address: Igathinathane.Cannayen@ndsu.edu (C. Igathinathane). Computers and Electronics in Agriculture 105 (2014) 54–65 Contents lists available at ScienceDirect Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag