Abstract Fruit production represents an important economic resource for many countries. The size and shape of fruits are crucial in production systems because they affect the market value. Particularly, in tomatoes (Solanum lycopersicum), the number of seed-containing cavities within each fruit, called locules, affects these attributes. The number of locules is also relevant for genotype-phenotype association studies designed to accelerate tomato breeding programs. Traditionally, the determination of the number of locules was performed through a visual inspection of a cross section of the fruit, a laborious, time-consuming, and highly subjective task. In this work, an automatic computer vision method for the identification and counting of the number of locules in tomato fruit images, called LocAnalyzer , is presented. The aim of LocAnalyzer is to speed up the processing time and reduce the subjectivity relative to the traditional manual approach for locule counting. The method was tested on two real tomato datasets. Promising results in terms of accuracy, precision, and recall measures were obtained, suggesting the potential usefulness of the approach for the development of a tool for the automatic measurement of other internal tomato attributes. Additionally, an experiment comparing the capacity of domain experts and LocAnalyzer in the identification of the number of locules was performed. The fact that expert- LocAnalyzer inter-group dispersion is smaller than expert-expert dispersion suggests that LocAnalyzer could be used as a gold standard for counting the number of locules. The proposed method, which is freely available, was implemented in the R programming language, and a web-based application was developed for online tests.

中文翻译：

---

LocAnalyzer：一种计算番茄果实中小室的计算机视觉方法

摘要 水果生产是许多国家的重要经济资源。水果的大小和形状在生产系统中至关重要，因为它们会影响市场价值。特别是，在西红柿 (Solanum lycopersicum) 中，每个果实中含有种子的空腔数量（称为小室）会影响这些属性。小室的数量也与旨在加速番茄育种计划的基因型-表型关联研究相关。传统上，细胞室数量的确定是通过对水果横截面的目视检查来进行的，这是一项费力、耗时且主观性很强的任务。在这项工作中，提出了一种自动计算机视觉方法，用于识别和计数番茄果实图像中的细胞核数，称为 LocAnalyzer。LocAnalyzer 的目标是加快处理时间并减少相对于传统手动方法进行细胞计数的主观性。该方法在两个真实的番茄数据集上进行了测试。在准确性、精确度和召回措施方面获得了有希望的结果，表明该方法在开发用于自动测量其他番茄内部属性的工具方面具有潜在的用处。此外，还进行了一项实验，比较了领域专家和 LocAnalyzer 在识别小室数量方面的能力。专家-LocAnalyzer 组间离散度小于专家-专家离散度这一事实表明，LocAnalyzer 可用作计算小室数量的黄金标准。所提出的方法是免费提供的，