Summary Resin canals produce and transport oleoresins that are important for tree defenses within the Pinaceae family. Rapid measurement techniques are needed to better understand how resin canal characteristics vary due to genetic and environmental effects. Here we describe a semi-automated microscopy imaging system that was built for quantifying longitudinal resin canals. Tree increment cores from 210 loblolly pine (Pinus taeda L.) trees were prepared into radial strips and the transverse surface of the samples polished with 400 and then 600 grit sandpaper. Each sample was imaged along its entire length (pith to bark) with the images collected from a monochrome camera connected to a Plan Fluorite 4× objective lens. The samples were imaged on the transverse surface via transmitted 850 nm near-infrared light directed at the radial surfaces of the samples. A total of 24 153 images were collected and then processed offline in Python using the Open Computer Vision Library (OpenCV) using a series of algorithms including contrast correction, noise removal, thresholding, contour identification, erosion, and dilation. A total of 24 491 resin canals were identified and their size quantified. The resin canals were assigned into annual rings and positioned within the earlywood or latewood of a ring using cross-correlation whereby a pseudo-density value was derived from the images and matched with density values measured by X-ray densitometry. Of the total resin canals identified, 51.5% were in the earlywood and 48.5% in the latewood, with the majority being detected in the earlywood in the first six years and the latewood in years 7 and above. This study represents the most information collected on the resin canals of loblolly pine. The detailed description of the hardware and image analysis methods should serve as a useful guide to others interested in imaging resin canals as well as other anatomical features.