Data and code from: High-throughput assessment of plant stand establishment, deedling vigor, and light interception in peanut using uav-based RGB and multispectral imagery
Data files
Jul 29, 2026 version files 570.98 KB
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HTP_Peanut_Manucript_Data.xlsx
561.35 KB
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README.md
9.64 KB
Abstract
In peanut, plant stand establishment, seedling vigor, and canopy growth are key determinants of crop performance; however, traditional ground-based assessment methods can be destructive, labor-intensive, and limited in throughput. This study evaluated the potential of vegetation metrics derived from unmanned aerial vehicle (UAV)-based RGB and multispectral (MS) imagery for high-throughput, non-destructive assessment of plant stand establishment, seedling vigor, and light interception in peanut. Seven runner-type peanut cultivars, each represented by two seed size classes (small and large), were used to generate variation in these traits. Within-row vegetation discontinuity-based plant stand ratings for estimating plant stand count (R2 = 0.81-0.90), together with canopy coverage for assessing seedling biomass (R2 = 0.77-0.82) and light interception (R2 = 0.96-0.98), were the best-performing vegetation metrics. Moreover, these vegetation metrics provided similar or greater cultivar separation compared with ground-based measurements. In contrast, several vegetation indices exhibited strong correlations with ground-based measurements but provided inconsistent cultivar rankings and statistical groupings. MS imagery outperformed RGB imagery for plant stand and seedling biomass assessment. Overall, these results demonstrate that UAV-derived canopy metrics provide reliable, high-throughput tools for early- to mid-season crop assessment and offer scalable alternatives to traditional ground-based approaches for agronomic, crop physiological, and plant breeding research.
Dataset DOI: 10.5061/dryad.xsj3tx9wp
Description of the data and file structure
This dataset was collected to evaluate high-throughput assessment of plant stand establishment, seedling vigor, and light interception in peanut using UAV-based RGB and multispectral imagery. The dataset includes ground-based measurements of plant stand count, seedling biomass, and intercepted photosynthetically active radiation, along with vegetation metrics derived from UAV-based RGB and multispectral imagery.
The dataset supports analysis of relationships among ground-based seedling vigor traits, canopy development, light interception, and image-derived vegetation features during early peanut growth. The Python script Within_Row_Gap_Based_emergence_rating.py, which provides code to quantify plant emergence using within-row vegetation gap-based estimation, has been uploaded separately to Zenodo.
Files and variables
File: HTP_Peanut_Manucript_Data.xlsx
Description: This file includes ground-based measurements of plant stand count, seedling biomass, and intercepted photosynthetically active radiation (IPAR), along with corresponding vegetation metrics derived from UAV-based RGB and multispectral imagery for each plot and measurement date.
The “Biomass” sheet contains ground-truth data for above-ground seedling dry biomass, along with corresponding vegetation metrics derived from UAV-based RGB and multispectral imagery.
The “Plant_Stand_Count” sheet contains ground-truth data for plant stand count, along with corresponding vegetation metrics derived from UAV-based RGB and multispectral imagery.
The “IPAR” sheet contains data for intercepted photosynthetically active radiation (IPAR), along with corresponding vegetation metrics derived from UAV-based RGB and multispectral imagery.
Vegetation index variables ending in “_RGB” indicate indices calculated from UAV-based RGB imagery, while variables ending in “_MS” indicate indices calculated from UAV-based multispectral imagery.
Missing Values
Empty cells indicate that data were unavailable for the corresponding sensor on a particular measurement date. These cells represent missing observations and should not be interpreted as zero or as values below the detection limit.
Variables
| Column Name | Description |
|---|---|
| Plot No. | Plot identification |
| Date | Sampling date |
| Biomass (g m^-2^) | Above ground dry biomass of seedling |
| Plant Stand count (Plant m^-2^) | Number of plants per unit land area |
| Plant_Stand_UAV_RGB | Plant emergence ratings estimated from RGB imagery |
| Plant_Stand_UAV_MS | Plant emergence ratings estimated from multispectral imagery |
| IPAR | Intercepted photosynthetically active radiation |
| NDVI | Normalized Difference Vegetation Index |
| PSSR (chl a) | Pigment Specific Simple Ratio–chlorophyll a |
| WDVI | Weighted Difference Vegetation Index (s = 0.5) |
| SAVI | Soil-Adjusted Vegetation Index (L = 0.5) |
| TSAVI | Transformed SAVI |
| OSAVI | Optimized Soil-Adjusted Vegetation Index |
| MSAVI | Modified SAVI (MSAVI2 form) |
| WDRVI | Wide Dynamic Range Vegetation Index (a = 0.2) |
| WDRVI2 | Wide Dynamic Range Vegetation Index (a = 0.2) |
| CIred_edge | Chlorophyll Index–red edge |
| MTVI | Modified Triangular Vegetation Index |
| MTVI2RE | Modified Triangular Vegetation Index 2 (red-edge adapted) |
| NDRE | Normalized Difference Red Edge |
| GNDVI | Green Normalized Difference Vegetation Index |
| PSSR C1 | Pigment Specific Simple Ratio–C1 |
| CIgreen | Chlorophyll Index–green |
| TVI | Triangular Vegetation Index |
| MTVI2 | Modified Triangular Vegetation Index 2 |
| BNDVI | Blue Normalized Difference Vegetation Index |
| PSSR (carotenoid) or NIR/B | Pigment Specific Simple Ratio–carotenoid |
| ARVI | Atmospherically Resistant Vegetation Index |
| SIPI | Structure Insensitive Pigment Index |
| MCARI2 | Modified Chlorophyll Absorption Ratio Index 2 |
| NARI | Normalized Anthocyanin Reflectance Index |
| ARI1 | Anthocyanin Reflectance Index 1 (adapted) |
| ARI2 | Anthocyanin Reflectance Index 2 |
| MTCI | MERIS Terrestrial Chlorophyll Index |
| TCARI | Transformed CARI |
| PSRI | Plant Senescence Reflectance Index |
| MCARI | Modified Chlorophyll Absorption Ratio Index |
| CARI | Chlorophyll Absorption Ratio Index |
| CRI1 | Carotenoid Reflectance Index 1 (adapted) |
| CRI2 | Carotenoid Reflectance Index 2 (adapted) |
| NDVIrededge | Normalized Difference Red Edge/Red |
| EVI | Enhanced Vegetation Index |
| EVI2 | Enhanced Vegetation Index 2 |
| GLI | Green Leaf Index |
| VARI | Visible Atmospherically Resistant Index |
| ExG | Excess Green Index |
| ExR | Excess Red Index |
| ExGR | Excess Green minus Red Index |
| NGRDI | Normalized Green–Red Difference Index |
| RGRI | Red–Green Ratio Index |
| VEG | Visible-band Greenness Index |
| RGBVI | RGB Vegetation Index |
| MRGBVI | Modified RGB Vegetation Index |
| CIVE | Color Index of Vegetation Extraction |
| PGC | Percent green cover or canopy coverage |
Code/software
The Python script “Within_Row_Gap_Based_emergence_rating.py” provides code to quantify plant emergence using within-row vegetation gap-based estimation. The script requires a binary vegetation mask GeoTIFF and a plot boundary file. The binary vegetation mask must be a raster image in which vegetation pixels are assigned a value of 1 and non-vegetation pixels are assigned a value of 0. It aligns crop rows based on row orientation, summarizes vegetation presence along the row direction, detects vegetation-absent segments exceeding a user-defined seed-spacing threshold, and exports plot-level gap metrics.
The script was written in Python and can be run using Python 3.9 or later. Required Python packages include numpy, pandas, geopandas, rasterio, shapely, and scikit-image. The input files required to run the script are a binary vegetation mask raster and a plot boundary shapefile or GeoPackage in the same coordinate reference system.
Main script output:
- rowwise_gap_detection_results.csv
Main output variables:
- Veg_Fraction
- Num_Gaps
- Total_Gap_m
- Gap_Fraction
- Skip_Estimate
- Emergence_Rating_1to9
- PixelSize_m
- MinVegWidth_m
- ExpectedSpacing_m
- MinGap_m
- RowAngle_deg
The provided script focuses specifically on the reproducible row-wise gap-detection step after binary vegetation mask generation.