live trees are living witnesses to history. They are standing for hundreds, even thousands of years, silently preserving ecological, agricultural, historical, and cultural heritage. However, the estimation of their monumental value can be difficult to assess because the age of most olive trees is usually unknown, making their calculation a very complicated task. Based on the previous work of Kabassi et al. [1], the sophisticated framework that combines three different multi‑criteria decision‑making methods—AHP, SAW, and VIKOR—has been implemented in a software called “Olea App,” which was developed to store information about olive trees and olive groves and to evaluate their “monumental” value. The knowledge of the tangible characteristics of an olive tree, such as the base perimeter, the perimeter at 1.30 meters height, and the overall height of the olive tree, together with intangible characteristics—such as the touristic, historical, and economic values of olive trees—define the main Key Performance Indicators (KPIs) for their monumental estimation. This kind of framework is especially vital for countries like Greece, where accurate valuation directly influences funding mechanisms and policies aimed at protecting these trees for future generations. Nevertheless, the estimation of the monumental value can be very demanding because people do not usually have the knowledge to make the calculations and the research that is needed to evaluate the monumental value.

In view of the above, this paper focuses on the design of a system the employ Artificial Intelligence for evaluating the “monumental” value of individual olive trees. The system accepts images of an olive tree together with its intangible characteristics as input. Using computer-vision algorithms, it analyzes each image and automatically extracts the olive tree’s base perimeter, the perimeter at 1.30 meters height and its total height. These geometric measurements are then combined with the intangible characteristics within the AHP‑SAW‑VIKOR framework, producing an automatically generated monumental‑value score for each tree.