Classification of sandalwood trunk area damaged by Zeuzera coffeae nietner in complex background

ObjectiveSandalwood is one of the precious tree species which is hard to survive and often suffers from diseases and pests. In order to improve the classification accuracy of sandalwood trunk regions, this research proposed a classification method for different sandalwood trunk regions which is attacked by coffee carpenter moth.

Method(1) First of all, we combined Gauss high-pass filter, Otsu segmentation method, 2G-R-B factor with morphological operation together to extract the sandalwood from background and divided the trunk into three regions, i.e. healthy region, pest region and excrement region using L^* channel and a^* channel in L^*a^*b^* system. (2) "Multi-texture" was proposed and "relative color" was used to extract image features. (3) We combined RBF-SVM and BP neural network together to identify different regions.

Result(1) The image segmentation algorithm proposed in this paper extracted the sandalwood from background and successfully divided it into healthy region, insect region and excrement region. (2) Compared with the traditional PCA processing, the classification accuracy obtained using "multi-texture" and "relative color" features was improved. Among them, the "multi texture" features were constructed by enlarging the difference of image features, which significantly increased the variance between classes. The "relative color" features reduced the influence of illumination on samples, thus reduced the intra class variance. (3) We compared different classification methods and found that only using RBF-SVM or BP neural network often caused low accuracy, the results were 74.44% and 81.11%, respectively. After using binary classification twice, the results were improved to 87.77% and 85.56%, respectively. The best classification result was obtained by combining RBF-SVM and BP neural network together, which was 91.11%.

ConclusionThis paper provides a new classification method for sandalwood trunk regions which is attacked by coffee carpenter moth by digital image processing. It provides a method for early identification and damage rate calculation of insect pests. It can reduce the harm caused by pests, improve the quality of heartwood, and provide a reference for the application of digital image technology in forestry.