Automated Colony-Forming Unit (CFU) Counting of Bacteria Using Digital Image Analysis Through Computer Vision with Python - Supplementary Data

This supplementary material corresponds to an Excel file (.xlsx) that documents key information associated with the development and validation of an automated bacterial colony counter based on computer vision, implemented in Python. The file contains two main sheets. Sheet 1 (Table S1) describes the photographic devices used to acquire images from the database, including device type, model, and technical specifications relevant to image analysis. Sheet 2 presents the results of automatic image processing, including the number of colonies detected by the algorithm, the manual reference count, true positives, false positives, and false negatives. Based on these data, the system performance metrics (precision, recall, and F-measure) are reported, as well as the processing time per image. This dataset supports the quantitative evaluation of the performance and computational efficiency of the proposed colony counter.本补充材料对应一份Excel（.xlsx）文件，该文件记录了基于计算机视觉、以Python实现的自动化菌落计数器开发与验证的相关关键信息。该文件包含两个主要工作表：工作表1（表S1）介绍了用于采集数据库图像的摄影设备，涵盖设备类型、型号及与图像分析相关的技术规格。工作表2展示了自动图像处理的结果，包括算法检测到的菌落数、人工参考计数、真阳性（True Positives）、假阳性（False Positives）与假阴性（False Negatives）。基于上述数据，本研究还报告了系统性能指标：精确率（Precision）、召回率（Recall）与F测度（F-measure），以及单幅图像的处理耗时。本数据集可用于对所提出的菌落计数器的性能与计算效率进行量化评估。