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.