3D-Printed Lightbox Delivers Lab-Quality Agar Plate Images for Machine Learning

Microbiologists have long known that the humble agar plate photograph is only as good as the lighting behind it. A new proof-of-concept study published in MicrobiologyOpen describes a low-cost, 3D-printed lightbox that transforms ordinary smartphone images of Petri dishes into standardized, high-quality photographs suitable for machine learning analysis. The device, which can be fabricated for roughly the price of a takeaway meal, promises to bring laboratory-grade image acquisition within reach of teaching laboratories, small research groups, and resource-limited settings that cannot afford commercial imaging systems costing tens of thousands of dollars.

The problem the researchers set out to solve is familiar to anyone who has ever photographed a Petri dish on a laboratory bench. Petri dishes and agar surfaces have reflective and optical properties that conspire against clean imaging. Photographs taken under routine room lighting or with handheld mobile phones are frequently marred by glare, reflections, shadows, and uneven illumination. These artifacts are not merely cosmetic. They substantially alter the pixel intensity distributions across agar plate images, introducing nonbiological variation, or noise, that presents serious challenges to automated analysis. As machine learning tools increasingly support automated colony counting, morphological comparison, and quantitative assessment of media color changes that signal metabolic processes such as pH shifts or hemolysis, the quality of the image captured at the point of acquisition ultimately determines the reliability of everything that follows.

The lightbox itself is an elegant exercise in accessible engineering. The initial model was developed in Tinkercad and refined in Autodesk Fusion, and the device incorporates an enclosed imaging chamber, an internal platform for positioning agar plates, removable diffusion and light-blocking components, and integrated light-emitting diode lighting. The chamber measures approximately 220.7 millimeters deep by 200 millimeters high by 200 millimeters wide and accommodates standard 90-millimeter Petri dishes, with a modular plate-support insert that can be modified for alternative plate sizes. An aperture at the top allows a smartphone camera to be reproducibly positioned directly above the plate, standardizing imaging distance, camera angle, and plate position between photographs.

Two independently configurable LED strips provide illumination from above and below the agar plate. The lower source delivers transmitted illumination through the agar, while the upper source provides reflected illumination to assist visualization of colony morphology, pigmentation, and media-associated changes. A removable one-millimeter diffuser plate, printed in white, softens the direct LED illumination from below, and a removable black blocker plate isolates individual lighting configurations. All printed components were fabricated in white polylactic acid filament, chosen because it is inexpensive, widely available, and compatible with most desktop 3D printers, while the white color enhances internal light reflection and promotes more uniform illumination. The main enclosure required approximately 700 to 900 grams of filament, corresponding to an estimated material cost of about AU$14 to AU$36, and the two LED light strips added roughly AU$15 each. Design files, assembly notes, and supporting workflow materials are freely available through an open-access repository to support reuse, modification, and local fabrication.

The performance evaluation compared three imaging conditions across five uncultured agar types: bile aesculin, nutrient, horse blood, mannitol salt, and MacConkey agar. Photographs were captured with the same iPhone 13 Pro under identical camera settings with flash disabled, either on a traditional black background, inside the lightbox with top lighting only, or inside the lightbox with combined top and bottom lighting. Analysis using Fiji/ImageJ examined 3D surface plots and pixel color histograms, in which reflective artifacts appeared as sharp peaks and uneven illumination showed up as greater dispersion of pixel colors. Across all plate conditions, lightbox imaging produced more even pixel color distributions than traditional photography, which routinely showed reflections from the phone itself and from the biosafety cabinet filter screen.

The bottom-lit configuration proved especially significant for hemolysis assessment. The American Society for Microbiology recommends that hemolytic reactions on blood agar be assessed with the light source positioned behind the plate to enhance visualization of zones surrounding colonies that may otherwise be faint. In cultured plate tests, the combined top and bottom lighting produced greater color and contrast differentiation between alpha, beta, and gamma hemolysis patterns, improving visual distinction between hemolytic classes. Transmitted illumination through the agar also improved visibility of small or low-contrast colonies that were less apparent under traditional imaging, while reducing reflective artifacts across both uncolonized regions and individual colonies.

To explore whether these improvements matter for machine learning, the team turned to QuPath, a freely available image analysis platform that lets users develop trainable pixel classifiers without coding expertise. Fourteen growth-positive horse blood agar plates, prepared from swabs of hospital staff mobile phones in an intensive care unit environment, were imaged under both conditions. Separate Random Trees pixel classifiers were trained for traditional and lightbox images of the same plates, with identical numbers of training annotations, allowing the effect of image acquisition conditions to be compared while holding the underlying colony distribution constant. The results were striking. For a representative matched plate, the classifier trained on the traditional image generated 159 detections, with visual review identifying false positives caused by printed plate text being detected as colony-like objects, uncolonized agar regions classified as colonies, and individual colonies fragmenting into multiple detections. The classifier trained on the corresponding lightbox image generated just 76 detections and showed fewer artifact-related errors.

An exploratory comparison with manual counting provided further support for the workflow’s potential. Across the 14 growth-positive plates, manual assessment identified 1014 colonies, compared with 992 from the semiautomated QuPath-assisted workflow following human review. The counts showed a strong linear association, with a Pearson correlation of 0.999 and a mean absolute error of just 2.4 colonies per plate. The authors are careful to frame these results as exploratory rather than validated, since classifier refinement and evaluation used the same images without an independent holdout data set. Larger independent data sets incorporating a broader range of organisms, colony densities, and imaging conditions will be needed before the workflow can be considered validated.

The study also highlighted stubborn artifacts that no lighting scheme can eliminate. Factory-printed text on the underside of agar plates, carrying agar type, batch, and expiry details, remained visible under all conditions and was particularly problematic on opaque media such as horse blood agar, where it appeared as darker, poorly defined regions resembling colonies or hemolytic changes. The researchers suggest training classifiers to recognize printed text as a separate image class, though this increases the annotation burden. The circular rim on the plate base similarly introduced edge-related artifact from light refraction and geometry changes, which can be managed by cropping to the agar area within the bottom rim.

Beyond the computational applications, the authors see real promise for microbiology education. Standardized photography could support more consistent image sets for practical teaching, assessment, and online learning resources, while giving students hands-on experience with digital image analysis and machine learning concepts. By comparing images captured under controlled and uncontrolled conditions, students can directly observe how imaging artifacts and data quality shape computational outputs, a principle that resonates across microbiology, histopathology, and radiology. The device also builds on earlier open-source work, including a prior imaging box assembled from cardboard, wood, and fabric, adding reusable 3D-printed components, a fixed camera aperture, and independently configurable dual illumination. Looking ahead, the authors note that alternative printing materials such as polyethylene terephthalate glycol may better withstand repeated disinfection in routine laboratory use, and that any change in material or surface finish should be reevaluated because it could alter illumination conditions and the performance of models trained under them. Future work should also assess color fidelity with standardized references and explore whether ambient light combined with smartphone night mode might offer an even simpler path to consistent imaging.

Subject of Research: A low-cost 3D-printed lightbox for standardized smartphone imaging of agar plates to support machine learning-assisted microbiological analysis.

Article Title: Improving Agar Plate Image Quality Using a Low‐Cost 3D‐Printed Lightbox: A Proof‐of‐Concept Evaluation for Machine Learning–Assisted Analysis

Article References: Improving Agar Plate Image Quality Using a Low‐Cost 3D‐Printed Lightbox: A Proof‐of‐Concept Evaluation for Machine Learning–Assisted Analysis. (n.d.). https://doi.org/10.1002/mbo3.70399

Image Credits: AI Generated

DOI: 10.1002/mbo3.70399

Keywords: 3D-printed lightbox, agar plate imaging, machine learning, colony counting, QuPath, pixel classification, hemolysis, smartphone microscopy, open-source hardware, microbiology education, image quality, automated colony counting