Computer vision-based dimension measurement system for olive identification

Abstract

Olive tree is an important portion of the human history of Mediterranean nations. On the other hand, local varieties are important for each producer regions and even countries. So, olive cultivars are important for agricultural production for these people. The traditional pomological identifiers of the olive trees based on fruits and leaves, also morphometric analysis of size, additionally shape elliptic analysis of endocarp. Because of this reason, in this study, for the ‘Picual’ olive cultivar identification, a fast and easy method was presented. For this aim, ‘Picual’ olive leaf, fruit, and stone dimension measurement values obtained from real-time video images. ‘Picual’ olive fruit, stone, leaf samples evaluated by using real-time computer vision measurements. Regression analysis was applied to the data which was obtained from real-time video and caliper measurements. According to the regression coefficient results, the regression between caliper length measurement (OLLM) and computer vision video length measurement (OLLCV) found as 98.9%, also the regression between caliper width measurement (OLWM) and computer vision video width measurement (OLWCV) found as 97.9% at ‘Picual’ leaves, additionally, the regression between caliper length measurement (OFLM) and computer vision video length measurement (OFLCV) found as 98.5% the regression between caliper width measurement (OFWM) and computer vision video width measurement (OFWCV) found as 98.1 % at ‘Picual’ fruits, at last, the regression between caliper length measurement (OSLM) and computer vision video length measurement (OSLCV) found as 86.2%, the regression between caliper width measurement (OSWM) and computer vision video width measurement (OSWCV) found as 75.3% at ‘Picual’ stones.