Dendro-AutoCount Enhanced Using Pith Localization and Peak Analysis Method for Anomalous Images

Dendrochronology serves as a vital tool for analyzing the long-term interactions between commercial timber growth and environmental variables such as soil, water, and climate. This study presents Dendro-AutoCount, an innovative image processing framework designed for identifying obscured tree rings in cross-sectional images of Pinus taeda L. The methodology integrates Hessian-based ridge detection with a weighted radial voting gradient method to precisely locate the pith. Following pith detection, the system performs radial cropping to generate directional sub-images (north, east, south, west), where rings are identified via intensity profile analysis, signal smoothing, and peak detection. By filtering outliers and averaging directional counts, the system effectively mitigates common visual interference from black molds, fungus, structural cracks, buds, knots, and cracks. Experimental results confirm the high efficacy of Dendro-AutoCount in processing anomalous tree ring images.