Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling.

Abstract

This paper presents a new methodology for the estimation of olive-fruit mass and size, characterized by its major and minor axis length, by using image analysis techniques. First, different sets of olives from the varieties Picual and Arbequina were photographed in the laboratory. An original algorithm based on mathematical morphology and statistical thresholding was developed for segmenting the acquired images. The estimation models for the three targeted features, specifically for each variety, were established by linearly correlating the information extracted from the segmentations to objective reference measurement. The performance of the models was evaluated on external validation sets, giving relative errors of 0.86% for the major axis, 0.09% for the minor axis and 0.78% for mass in the case of the Arbequina variety; analogously, relative errors of 0.03%, 0.29% and 2.39% were annotated for Picual. Additionally, global feature estimation models, applicable to both varieties, were also tried, providing comparable or even better performance than the variety-specific ones. Attending to the achieved accuracy, it can be concluded that the proposed method represents a first step in the development of a low-cost, automated and non-invasive system for olive-fruit characterization in industrial processing chains.

Plant phenotyping relevance

オリーブ果実の質量・サイズという植物器官形質を、画像解析、数学的形態学、統計的閾値処理で推定する手法を開発し、外部検証しており、フェノタイピング手法が研究の中心である。

Code and data availability

The paper describes laboratory olive images, ground-truth annotations, and a MATLAB segmentation/estimation pipeline, but contains no data or code availability statement, no public deposit, and no author-provided URL for datasets, images, ground truths, or scripts. The only URLs present are the license and cited IOC/US

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