Phytograph

A desktop application for measuring, comparing, and modeling plant architecture from LiDAR scans — built for plant scientists who work with point clouds, meshes, and procedural plant models.

📖 Full documentation & user guide: https://plantsimulationlab.github.io/Phytograph/

Phytograph runs on macOS (Apple Silicon and Intel), Windows 10/11, and Linux (x64, glibc 2.38 or newer). It ships as a single self-contained app with its own embedded scientific Python environment — you don't need to install Python, Conda, or anything else.

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Download & install

Get the latest installer from the Releases page:

Platform	Download
macOS (Apple Silicon — M1/M2/M3/M4)	Phytograph-arm64.dmg
macOS (Intel)	Phytograph-x64.dmg
Windows 10/11	Phytograph-Setup.exe
Linux (x64, glibc 2.38+)	Phytograph-x86_64.AppImage

• macOS — open the .dmg, drag Phytograph into Applications, and launch it. The build is signed and notarized by Apple, so it opens with a normal double-click.

• Windows — run the installer. If SmartScreen warns you, choose More info → Run anyway. If the app opens but reports that Windows blocked one of its components, see the install guide.

• Linux — run it from a terminal: chmod +x Phytograph-x86_64.AppImage then ./Phytograph-x86_64.AppImage. Double-clicking often does nothing — GNOME Files and similar managers refuse to launch executables. (Also needs FUSE; on a minimal install use --appimage-extract-and-run.)

  Requires glibc 2.38 or newer — Ubuntu 23.10+ (24.04 LTS), Debian 13, Fedora 39+, or a current rolling release. Check yours with ldd --version. It does not run on Ubuntu 22.04 LTS, Debian 12, or RHEL/Rocky/Alma 8–9: the app window opens but the compute backend cannot start, and the log shows a loader error naming GLIBC_2.38. See the install guide.

The first launch takes about 30 seconds while the bundled Python environment unpacks itself; subsequent launches are instant.

Full step-by-step install instructions (including first-launch notes for each OS) are in the Install guide.

Updates

Phytograph checks for updates on launch and can also be updated on demand via Check for Updates… — in the Phytograph app menu on macOS, or under Help on Windows and Linux. When a newer release is available it downloads and installs in place — no need to return here to re-download.

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What it does

• Import LiDAR scans — drag and drop .las, .laz, .e57, .ptx, .ply, .pcd, or ASCII (.xyz, .txt, .csv, .pts, .asc, .ascii) point clouds into a 3D viewer that handles tens of millions of points. RIEGL .riproject and .PROJ scanner projects import directly, and an SBET trajectory places a moving-platform scan. Save the whole scene as a .phyto project.
• Clean and prepare — transform, crop, erase, filter, resample, and cross-section a cloud, compute normals and new scalar fields, then backfill the sky/miss rays that leaf area density depends on.
• Register and compare — auto-register rotated scans, refine with ICP, and stitch overlaps into one cloud, with cloud-to-mesh distance statistics (mean, median, percentiles, and coverage at fractions of the bounding-box diagonal).
• Segment scans — classify ground with a cloth simulation filter, separate wood from leaf with a machine-learning model, split a terrestrial or airborne plot into individual trees, and label a herbaceous plant's soil, stem, and individual leaflets — then carry the labels through the rest of the pipeline.
• Label points by hand — paint your own classes with a lasso or brush to correct a classifier or build ground truth, with per-class counts and undo.
• Measure leaf angles — triangulate a leaf-on scan into leaf surfaces and read their orientations: inclination and azimuth distributions, canonical de Wit fits, and the G(θ) that leaf area density depends on. The same triangulation also reconstructs branch and canopy surfaces.
• Extract skeletons — pull topological skeletons out of woody scans, with branch order colored by Strahler number and total length reported.
• Build QSMs — reconstruct dormant trees as connected cylinders with fitted radii, segment continuous shoots, and classify them by shoot rank, with woody volume, trunk diameter, and per-rank metrics. Add leaves by phyllotaxis and match a measured leaf-angle distribution.
• Run a tree inventory — turn a segmented plot into a tree list of stem position, DBH, stem curve, height, crown base, and crown size, summarize the stand (stems/ha, basal area, QMD, Lorey's height, canopy cover, biomass), and build a QSM for every tree in one run.
• Measure leaf and branch area — invert overlapping scans into a voxel grid of leaf area density (m²/m³) with its vertical profile and LAI, and fit crown shapes (ellipsoid, prism, cone, alpha shape) for height and volume.

See the User Guide and Workflows for task-by-task walkthroughs.

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Building from source / contributing

The instructions above are for using Phytograph. If you want to build it from source, run a development instance, or contribute, see the Developer documentation, which covers the setup and dev loop, the architecture, and the release process.

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Phytograph is developed at the Bailey Lab at UC Davis. Its procedural plant generation and scan-simulation features are powered by the Helios plant-modeling framework.