A lightweight PySide6 desktop application for counting early-season crop stands (sunflower, corn, and other row crops) from large RGB GeoTIFF orthomosaics.

Designed for:

• Research plot trials
• Emergence assessment (e.g., ~7 DAE sunflower)
• Early corn stand counts
• High-throughput field phenotyping workflows

Optimized for large TIFFs (hundreds of MB to multi-GB) with built-in pyramid support.

• Define plots by clicking start/end points for each row
• Configurable number of rows per plot
• Configurable row spacing
• Optional fixed row length mode (e.g., standardized 20 ft strips)
• Adjustable row AOI width (ft)

• Excess Green (ExG) vegetation segmentation
• Otsu thresholding inside row AOI
• Morphological cleanup
• Connected components plant detection
• Cluster/double detection heuristic
• Raw and adjusted counts

Per Row:

• Row length (ft)
• Adjusted plant count
• Raw plant count
• Plants per foot

Per Plot:

• Total adjusted plants
• Total raw plants
• Plants per acre (adjusted + raw)
• Plot area (ft²)

• Built-in pyramid (overview) creation
• Option to build overviews in-place or on a copy (copy recommended)
• Smooth zoom and pan for large mosaics

• rows.csv — row-level data
• plots.csv — plot-level summaries
• annotated_overview.png
• Individual plot images: plot_####_raw.png and plot_####_annot.png

⚠️ The original TIFF is never modified by annotations.

• Python 3.11 or 3.12+

Create and activate a virtual environment (recommended):

python -m venv venv
venv\Scripts\activate        # Windows
# source venv/bin/activate   # macOS/Linux

Install dependencies:

pip install pyside6 rasterio opencv-python numpy scikit-image

Or using requirements.txt:

pyside6
rasterio
opencv-python
numpy
scikit-image

pip install -r requirements.txt

python Sunf_count_GUI.py

• Go to File → Open GeoTIFF
• Set your Output Folder

• Check Ensure overviews (pyramids) on open
• Select Build overviews on a COPY (recommended)

This ensures smooth navigation on large images.

• Rows per plot
• Row spacing (inches)
• Row AOI width (ft)
• Fixed row length (optional)
• Detection parameters (area, circularity, cluster factor)

Click to define row endpoints:

• Row 1 → start, end
• Row 2 → start, end
• … After the final required click → preview computes automatically

Additional controls:

• Show Preview (Left) — visually inspect detection
• Mouse wheel — zoom
• Right-click drag — pan
• Reset View — return to full overview

1. Compute Excess Green index:

   ExG = 2G - R - B
   

2. Otsu thresholding within the row AOI
3. Morphological closing
4. Remove small objects
5. Connected components labeling

For each row:

• Compute median blob area
• If blob_area > cluster_factor × median:
  • Flag as cluster
  • Assign a size multiplier (capped)
• Adjusted counts sum multipliers per row

• Sunflower emergence (~7 days after planting)
• Corn early stand counts
• Multi-row research plots
• Fixed-length sampling strips (e.g., 15 ft, 20 ft, 30 ft)

Place images in docs/images/ and reference them below.

output_folder/
│
├── rows.csv
├── plots.csv
├── annotated_overview.png
└── plots/
    ├── plot_0001_raw.png
    ├── plot_0001_annot.png
    ├── plot_0002_raw.png
    └── ...

.
├── Sunf_count_GUI.py
├── README.md
├── requirements.txt
├── LICENSE
└── docs/
    └── images/

Specify your license here (e.g., MIT, Apache 2.0).

If you use this tool in your research, please consider citing or acknowledging the source. Contributions and issues are welcome via GitHub.