Canopy analysis and thermographic abnormalities determination possibilities of olive trees by using data mining algorithms

Abstract

In order to take the appropriate tree protection measures, it is crucial to determine and track abnormalities that may occur in olive trees in time to time for many reasons. Abnormalities start in different sections of the trees, depending on the environmental effects of the olive tree, with a specific impact like fungal diseases, drought, etc. after a certain age especially in non-resistant species. Protection steps may be taken when abnormalities are apparent or predictable in certain olive trees, using some external indicators. However, when abnormalities formed within trees cannot be identified externally, there is a sudden breakdown and overthrow of valuable properties, such as monument trees. In the literature, various devices and methods are explained to classify these defects in different trees. By the way, in this research, a non-destructive inspection method (thermography) was clarified and used to assess anomalies in old olive trees without damage in the interior. According to the results of average thermal data, 60, 400, 600 year-old olive trees, 60-40, 70-30 and 80-20 learning-prediction data rates decision tree and random forest results according to normal and abnormal thermal difference, the thermal range was found as 35.95 ℃ at 60 year-old tree, also it was found as 36.25 ℃ at 400 year-old tree and it was found as 38.25 ℃ at 600 year-old tree.