Discrimination of bark from wood chips through texture analysis by image processing

  • Authors:

  • James R. Wooten;S. D. Filip To;C. Igathinathane;L. O. Pordesimo

  • Affiliations:

  • Department of Agricultural and Biological Engineering, Mississippi State University, 130 Creelman Street, Mississippi State, MS 39762, USA;Department of Agricultural and Biological Engineering, Mississippi State University, 130 Creelman Street, Mississippi State, MS 39762, USA;Department of Agricultural and Biosystems Engineering, North Dakota State University, 1221 Albrecht Blvd, Fargo, ND 58102, USA;Department of Agricultural and Biological Engineering, Mississippi State University, 130 Creelman Street, Mississippi State, MS 39762, USA and ADM Alliance Nutrition, 1000 North 30th Street, Quinc ...

  • Venue:
  • Computers and Electronics in Agriculture
  • Year:
  • 2011

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Abstract

Utilization of wood chips for bioenergy requires classification and segregation of the constituents of the chipped mass to help optimize energy conversion. Wood chips obtained from processes such as forest thinning can contain a considerable amount of material other than wood chips, such as bark. An image processing algorithm was developed to discriminate bark from wood chips. The algorithm involved object identification, image capture, single value decomposition to describe wood texture evident in grayscale image with a single numerical value, and application of logistic models involving the single values representative of wood texture to predict whether a chip is bark. The percentage of correct predictions using this system was about 98%.