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The objective of this study was to develop a computer-aided method to quantify the obvious degree of growth ring boundaries of softwood species, based on data analysis with some image processing technologies. For this purpose, a 5× magnified cross-section color micro-image of softwood was cropped into 20 sub-images, and then every image was binariz...
Context in source publication
Context 1
... to the IAWA list of microscopic features for softwood identification [17], Tsuga chinensis var. forrestii (Fig 1) is always identified as having distinct growth ring boundaries, but Podocarpus neriifolius (Fig 2) may be recognized as having either obvious growth ring boundaries or not obvious growth ring boundaries [18]. The presence of growth ring boundaries in Podocarpus neriifolius varies from person to person, due to definitions of "growth ring boundaries = growth rings with an abrupt structural change at boundaries between them" and "growth ring boundaries indistinct or absent = growth rings boundaries vague and with marked gradual structural changes" being qualitative, not quantitative, which generates a serious problem for a wood identification researcher. ...
Citations
... A total of 95 microscopic slides were collected from Wood Collections, Chinese Academy of Forestry, involving 8 families of Ginkgoaceae, Araucariaceae, Podocarpaceae, Cephalotaxaceae, Taxaceae, Pinaceae, Taxodiaceae, and Cupressaceae(Lin et al. 2020). Imaging was performed with a digital camera (LEICA DMC4500) mounted on a light microscope (LEICA DM2000 LED)(Lin et al. 2020). ...
... A total of 95 microscopic slides were collected from Wood Collections, Chinese Academy of Forestry, involving 8 families of Ginkgoaceae, Araucariaceae, Podocarpaceae, Cephalotaxaceae, Taxaceae, Pinaceae, Taxodiaceae, and Cupressaceae(Lin et al. 2020). Imaging was performed with a digital camera (LEICA DMC4500) mounted on a light microscope (LEICA DM2000 LED)(Lin et al. 2020). Tangential section images were captured at 100 magni cation using an objective lens and eyepiece magni ed by 10 times each, resulting in a resolution of 2560 × 1920 pixels. ...
In order to achieve rapid acquisition, identification and measurement of the average ray height of softwood based on tangential section photographs, a new method is proposed. Firstly, labels the digital image of the softwood tangential section with the 100 magnification, that is, mark the rays and scales on the image, and establish the dataset; Secondly, the dataset is randomly divided into training set and validation set. YOLOv5s is used for model training to obtain the best target recognition model of rays and scale. The experimental results show that the model trained with YOLOv5s can achieve 93.5% accuracy, 95.6% recall and 96.7% average accuracy in the validation set; Thirdly, using the YOLOv5s trained model, a visual program for automatically calculating the ray height and obtaining the ray characteristics of softwood is designed, which lowered the threshold for wood identification workers to use such software.
