University of Huelva, Department of Electronic Engineering, Computer Systems and Automation, La Rábida, Palos de la Frontera, 21819 Huelva, Spain.
Sensors (Basel). 2018 Sep 3;18(9):2930. doi: 10.3390/s18092930.
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.
本文提出了一种新的基于图像处理技术的方法，用于估计橄榄果的质量和大小，其特征在于橄榄果的长轴和短轴长度。首先，在实验室中对 Picual 和 Arbequina 两种品种的不同组橄榄果进行拍摄。开发了一种基于数学形态学和统计阈值的原始算法，用于对获取的图像进行分割。通过将从分割中提取的信息与客观参考测量线性相关，为三个目标特征（每个品种各三个）建立了估计模型。通过外部验证集评估模型的性能，在 Arbequina 品种的情况下，对于长轴、短轴和质量的相对误差分别为 0.86%、0.09%和 0.78%；类似地，在 Picual 品种的情况下，相对误差分别为 0.03%、0.29%和 2.39%。此外，还尝试了适用于两种品种的全局特征估计模型，它们提供了与特定品种模型相当甚至更好的性能。根据所达到的精度，可以得出结论，该方法代表了在工业加工链中开发低成本、自动化和非侵入式橄榄果特征描述系统的第一步。
