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Description of olive morphological parameters by using open access software.

Konstantinos N Blazakis¹, Maria Kosma¹, George Kostelenos²

¹Department of Horticultural Genetics and Biotechnology, Mediterranean Agronomic Institute of Chania (MAICh), Alsyllio Agrokipiou, PO BOX 85, 73100 Chania-Crete, Greece.

|December 15, 2017

Summary

This summary is machine-generated.

This study introduces a semi-automatic method using image analysis to quantify olive morphology, aiding cultivar identification. The approach efficiently describes plant traits for various crops.

Keywords:

Endocarp Fruit Image analysis Leaf Morphological analysis Morphological analysis of crop species Olive

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Area of Science:

Agricultural Science
Computer Vision
Plant Morphology

Background:

Morphological analysis of olive (Olea europaea) leaves, fruits, and endocarps aids cultivar characterization.
Molecular markers are increasingly used for their diagnostic efficiency and independence from environmental factors.

Purpose of the Study:

To develop and present a semi-automatic methodology for quantifying morphological parameters of olive cultivars.
To establish a robust and efficient method for crop morphology description.

Main Methods:

Utilized computing and image analysis tools to create semi-automatic algorithms.
Developed intuitive mathematical descriptors to quantify morphological features.
Examined quantitative and qualitative characters including size, shape, symmetry, and surface roughness.

Main Results:

Successfully quantified numerous fruit, leaf, and endocarp morphological features.
Demonstrated the methodology's performance on Greek olive cultivars (fruits, leaves, endocarps).
Applied the approach to describe morphologies of other crop species like tomato, grapevine, and pear.

Conclusions:

The proposed semi-automatic methodology offers an efficient and robust way to describe crop morphologies.
This approach complements traditional methods and molecular markers in plant science.