Online system for automatic tropical wood recognition

There are more than 3000 wood species in tropical rainforests, each with their own unique wood anatomy that can be observed using naked eyes aided with a hand glass magnifier for species identification process. However, the number of certified personnel that have this acquired skills are limited due to lenghty training time. To overcome this problem, Center for Artificial Intelligence & Robotics (CAIRO) has developed an automatic wood recognition system known as KenalKayu that can recognize tropical wood species in less than a second, eliminating laborious manual human inspection which is exposed to human error and biasedness. KenalKayu integrates image acquisition, feature extraction, classifier and machine vision hardware such as camera, interfaces, PC and lighting. Grey level co-occurrence matrix (GLCM) is used for feature extraction. The features are trained in a back-propagation neural network (BPNN) for classification. This paper focusses more on the database development and the online testing of the wood recognition system. The accuracy of the online system is tested on different image quality such as image taken in low light condition, medium light condition or high ligh