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.