Classification of eucalyptus leaves: Combining color histogram feature extraction and decission tree algorithm - Agustiani, Sarifah; Hidayat, Rahmat; Arifin, Yoseph Tajul; Haryani; Marlina, Siti (Institute of Computer Science (IOCS), 2024)
This research proposes an automatic approach to identify eucalyptus species based on leaf images using color histogram feature extraction and the Decision Tree algorithm. Eucalyptus is known as one of the most productive plants in the world with various uses in the timber, biofuel and pharmaceutical industries. However, its wide environmental adaptability and rapid growth pose challenges in identification and management. The proposed approach focuses on the use of Artificial Intelligence (AI) technology and image analysis to solve the identification problem. The color histogram feature extraction method is used to extract visual information about the color distribution of eucalyptus leaves. The Decision Tree algorithm is used to build a classification model based on the extracted features. Model evaluation is carried out using accuracy, precision, recall and F1-score metrics. The results showed that this approach was effective in identifying eucalyptus species, with a high level of accuracy. In addition, the development of this method offers opportunities for further applications in various fields, including forest mapping, mobile applications, and the timber industry. By combining advances in AI and image analysis, this research has the potential to become an important cornerstone of nature conservation and environmental sustainability efforts, and help strengthen natural resource management globally