18 December 1996 Neural network recognition of the conifer seedling root collar
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In recent work, we have demonstrated a prototype machine vision seedling inspection system which shows strong promise for automating production-line grading. Precise morphological measurements and accurate grade assignment require reliable identification of the seedling root collar location. The large variability of seedling morphology makes automatic root collar location the most challenging aspect of machine vision seedling inspection. This function is currently achieved using a heuristic algorithm which relies on many operator-controlled parameters to extract root collar location cues based on seedling shape. Artificial intelligence techniques, specifically, neural networks, have yielded excellent performance in similar pattern recognition applications. Neural networks were developed to locate the seedling root collar in digital images acquired by a machine vision inspection system. Several neural network architectures and input feature sets are evaluated. Input features consist of those used by the heuristic algorithm, plus additional features extracted from each line in the seedling image. The performance of several neural networks was superior to that of the heuristic algorithm. Good performance was achieved by networks which used local (single line) features along with normalized line number as inputs. A hierarchical network which took inputs from 15 lines over a 140-mm window provided improved performance in one case. The best networks identified the root collar location with an average error of less than 1 mm and an error standard deviation of 12 mm.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael P. Rigney, Michael P. Rigney, Glenn A. Kranzler, Glenn A. Kranzler, } "Neural network recognition of the conifer seedling root collar", Proc. SPIE 2907, Optics in Agriculture, Forestry, and Biological Processing II, (18 December 1996); doi: 10.1117/12.262851; https://doi.org/10.1117/12.262851


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