Computer vision in image detection case study of tree damage type using convolutional neural network (CNN) algorithm

• Main Authors: Nopriyanto, Z., Andrian, R., Safe’i, R.
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Artificial neural networks; Computer vision; Damage detection; Error detection; Feature extraction; Image acquisition; Image compression; Image detection; Image processing; Neural networks
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0208170

So far, the identification of 16 types of tree damage is still following the guidelines stated in the Forest Health Monitoring method. The types of tree damage can be recognized by human vision, as well as by computers. Computer vision allows computers to identify things that humans can remember. In this case, the study can be realized with computer vision to make work easier. This study aimed to identify 16 types of damage in Forest Health Monitoring using image data or photos with computer vision. The stages of this research are image acquisition (image acquisition), image processing (image preprocessing), and feature extraction (feature extraction). The results showed that computer vision could identify images in JPG/JPEG (Joint Photographic Experts) format, assisted by the Convolutional Neural Network (CNN) algorithm. The percentage value of success using the CNN algorithm reaches 99.06%, with an error detection of 0.94%. However, several classes were incorrectly identified, indicating that the dataset needs improvement. Thus, it can be concluded that identifying 16 types of damage using image data or photos with computer vision has been successfully carried out.