Crowe Vision
Computer Vision Precision for Mycology Analysis
Analyze mycelium samples with precision—detect contamination, assess health, and optimize your cultivation with advanced computer vision technology.
Advanced Mycelium Analysis
Precision diagnostics powered by computer vision technology
Health Assessment
Detect contamination risks and assess overall mycelium health with precision computer vision analysis.
Growth Tracking
Monitor mycelium development over time with comprehensive timeline analysis and progress visualization.
Cultivation Insights
Gain valuable insights on substrate composition, environmental conditions, and optimization techniques.
Imaging Modalities
Crowe Vision supports multiple imaging methods to provide comprehensive analysis
| Modality | Why it matters | Field tips |
|---|---|---|
|
RGB stills & video
|
Baseline for colour/texture cues such as whitening, surface rhizomorphs, pin colour change. | Mount industrial cameras or good phone cams; 8‑12 MP is enough if FOV is tight. |
|
Multispectral / hyperspectral
|
Detect early contamination, moisture pockets, CO₂ stress before they appear in RGB. | Inexpensive 5‑band ag‑cams (near‑IR, red‑edge) work; calibrate weekly. |
|
Depth / point cloud
|
Measure cap height & biomass non‑destructively. | Commodity depth cameras (RealSense D455, Azure Kinect) give < 3 mm error at 40–70 cm. |
|
Microscope JPEG/PNG tiles
|
Clamp‑connection, hyphal autolysis, strain‑competition traits. | Collect at 40× & 100×; save magnification meta in EXIF. |
Data Labeling Guidelines
Standardized labeling ensures consistent and accurate analysis results
| Level | Label type | Example |
|---|---|---|
|
Frame-level
|
growth_stage = "pinning" or "flush-1" | Collected manually once per tray per day. |
|
Object-level
|
Bounding boxes or polygons around pins, caps, contamination spots | Roboflow "Mushroom Growth Stages" has 157 images with YOLO/COCO masks to copy the format. |
|
Pixel-level
|
Semantic masks for mycelium vs. substrate vs. mold | Needed for hyphal density estimation. |
|
Temporal
|
Timestamp, substrate lot, strain, room ID, temp/humidity/CO₂ sensor snapshot | Lets model learn growth-rate differences. |
Growth Stage Requirements
Specific image counts needed for each growth stage to train accurate models
| Stage | RGB frames | Pixel masks | Depth maps | Microscope tiles |
|---|---|---|---|---|
|
Spawn run (colonisation)
|
3,000 | optional | — | — |
|
Pinning
|
2,000 | nice-to-have | — | — |
|
Flush ready (cap ≤ 30 mm)
|
1,500 | recommended | ✓ | — |
|
Mature flush / harvest
|
2,000 | recommended | ✓ | — |
|
Contamination events
|
≥ 300 | critical | — | if hyphal |
Quality & Workflow Tips
Best practices for collecting high-quality data for optimal results
-
Consistent lighting — install 5,000 K LED strips; flicker kills model accuracy.
-
Weekly calibration — use a colour-checker so whiteness/greying cues stay true.
-
Version your dataset — keep a data card listing collection dates, camera types, augmentation steps.
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Balance the classes — pinning images are rarer, so oversample them or shoot extra time-lapse for that window.
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Validate properly — test on unseen rooms or harvest cycles; fungal growth is sensitive to micro-climate variations.
Research Resources
Supplementary datasets to enhance your Crowe Vision experience
Crowe Vision Advantage: While these public datasets can aid in mycology research, Crowe Vision's proprietary models are pre-trained on our extensive curated dataset with 25,000+ expertly labeled samples across diverse growth conditions. Our platform delivers clinical-grade accuracy that surpasses what can be achieved with open datasets alone.
Academic and open-source datasets that complement Crowe Vision's proprietary analysis:
| # | Dataset | What you get | Size | License | Access |
|---|---|---|---|---|---|
| 1 |
Roboflow "Mushroom Growth Stages"
|
157 RGB images (640×640) with YOLO/COCO masks covering inoculation → flush | ≈ 90 MB | CC-BY-4.0 | Dataset Page |
| 2 |
Realistic Synthetic Mushroom Scenes (SMSD)
|
15,000 synthetic RGB images + masks + 3-D pose | ≈ 9 GB | MIT (code) CC-BY-NC-4.0 (images) |
GitHub |
| 3 |
Synthetic Fungi Time-Aligned
|
Fully temporally-aligned spore → mycelium image sequences (synthetic) | ≈ 4 GB | CC-BY-4.0 | arXiv |
| 4 |
Mycelium-6 (Roboflow)
|
210 microscope & macro frames of healthy mycelium with instance masks | ≈ 120 MB | CC-BY-4.0 | Roboflow |
| 5 |
Mycelium Clamp-Connection YOLO set
|
4,472 microscope JPGs (10 edible strains) labelled "Clamp" vs "Autolysis" | ≈ 2 GB | Research-only (free) | Study Link |
| 6 |
M18K RGB-D Mushroom Segmentation
|
423 RGB-D pairs (RealSense D405) + instance masks for Agaricus spp. | ≈ 3 GB | MIT | GitHub |
| 7 |
North-American Mushrooms (classification)
|
8K field photos / 23 genera (no stage labels; good for pre-training) | ≈ 2 GB | CC-BY-SA-4.0 | Roboflow |
Data Format Guide
Understanding Crowe Vision's internal data structures for research purposes:
COCO-Compatible Structure
Crowe Vision uses an enhanced version of the COCO format with proprietary extensions
crowe_dataset/ ├── images/ │ ├── 2025-05-01T08-00-00_inoculation.jpg │ ├── 2025-05-04T08-00-00_colonisation.jpg │ └── 2025-05-08T08-00-00_pinning.jpg ├── annotations_coco.json # Enhanced with additional properties └── metadata.csv # Environmental variables and cultivation data
For academic researchers:
When importing your own data to compare with Crowe Vision analyses, we recommend:
-
Use consistent naming conventions for tracking growth over time
-
Include timestamps and environmental data for contextual analysis
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Maintain EXIF data when possible for imaging equipment calibration
-
Export metadata in CSV format for compatibility with our analytics
Model Extension Framework
For researchers and advanced users looking to enhance Crowe Vision's capabilities
Custom Model Integration
Extend Crowe Vision with specialized detection modules for your unique requirements:
Crowe Vision supports plug-in architecture for specialized detection models. Custom models work alongside our core analysis engine, adding capabilities specific to your research or production needs.
Extension Capabilities:
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Strain-specific growth pattern detection
-
Custom contamination recognition for your environment
-
Research-specific metrics and measurements
-
Integration with proprietary imaging hardware
Enterprise Compatibility:
Custom models trained by research partners can be deployed alongside Crowe Vision's core system, with several integration options available for Enterprise clients:
- Secure API integration
- On-premises deployment options
- Containerized model execution
- Model registry and versioning
Research Partnership Program
Collaborate with our team on advancing mycology computer vision:
Crowe Vision partners with academic institutions on research initiatives. Qualified research teams receive access to specialized tools, sample datasets, and technical support for integrating our technology into academic studies.
For commercial cultivators and industrial mycology operations, we offer joint research opportunities that combine your domain expertise with our advanced computer vision technology to solve specific production challenges.
Researchers who develop novel detection models can contribute to our specialized model library. Accepted contributions are integrated into the platform with appropriate attribution and potential revenue sharing for premium features.
Organizations with large, annotated datasets of fungal cultivation imagery can explore data sharing agreements that benefit both parties. These collaborations help expand the diversity and robustness of our analysis capabilities.
Upload Sample
Upload a clear image of your mycelium sample for computer vision analysis.
Analysis Options
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Analysis Method
Advanced AI analysis takes approximately 20-30 seconds
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Sample Analysis
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Timeline
Your latest sample history:
| Date | Strain | Type | Status | Confidence |
|---|---|---|---|---|
| Apr 30, 2025 | Lion's Mane | Grain Jar | Healthy | 98.4% |
| Apr 27, 2025 | Shiitake | Agar Plate | Healthy | 95.1% |
| Apr 22, 2025 | Reishi | Substrate | Contained | 87.3% |
Contamination Detection
Identifies potential contaminants and assesses risk levels
Growth Tracking
Monitor colonization rates and predict growth timelines
Health Assessment
Evaluates overall mycelium health and vitality scores