Multispectral Data Analysis and Visualization

Provides tools for processing, analyzing, and visualizing spectral data collected from 3D laser-based scanning systems. Supports applications in agriculture, forestry, environmental monitoring, industrial quality control, and biomedical research. Enables evaluation of plant growth, productivity, resource efficiency, disease management, and pest monitoring. Includes statistical methods for extracting insights from multispectral and hyperspectral data and generating publication-ready visualizations. See Zieschank & Junker (2023) and Saric et al. (2022) for related work.

PhenoSpectra

PhenoSpectra is an R package designed for processing, analyzing, and visualizing spectral data collected from 3D laser-based scanning systems. This package supports data from various domains, including agriculture, forestry, environmental monitoring, industrial quality control, and biomedical research.

Key Features

• Data Handling:

  • Automated reading, merging, and preprocessing of spectral datasets.
  • Splitting timestamp columns into separate date and time components.
  • Group-based assignment of biological replicates (Bio.Rep).

• Quality Control:

  • Detection of missing values and outliers.
  • Flexible handling of irregularities with options to replace, remove, or impute data.
  • Group-specific outlier detection using statistical methods.

• Feature Selection & Prediction:

  • Selection of the most discriminative spectral variables using FDR correction.
  • Prediction of Spectral Disease Severity (SDS) using linear regression models.

• Customizable Workflow:

  • Supports group-based analyses for treatments or other categorical variables.
  • Generates clean, publication-ready datasets and summary reports.

Installation

To install the PhenoSpectra package directly from GitHub:

1. Ensure you have the devtools package installed:

   install.packages("devtools")
   

2. Install PhenoSpectra:

   devtools::install_github("bayer-int/PhenoSpectra")
   

File Structure

Below is an overview of the directory structure of the PhenoSpectra package:

• R: R functions (e.g., reads.R, qaqcs.R, feature_selection.R, predict_SDS.R)
• man: Documentation files (auto-generated by Roxygen2)
• data: Example datasets (if applicable)
• Demo: Demo input/output files for testing functions
• DESCRIPTION: Package metadata
• NAMESPACE: Package imports and exports
• README.md: Project overview and usage
• LICENSE: Licensing information
• inst: Installation-related files (if required)
• vignettes: Extended examples and tutorials (if applicable)

Usage Examples

1. Using reads()

merged_data <- reads(
  directory = "Demo",
  pattern = "input",
  output_path = "Demo/processed_data.xlsx"
)

2. Using qaqcs()

result <- qaqcs(
  file_path = "Demo/raw_data.xlsx",
  output_path = "Demo/cleaned_data.xlsx",
  handle_missing = "impute",
  handle_outliers = "impute",
  group_by_col = "treatment"
)

# Access the cleaned data and summary table
cleaned_data <- result$cleaned_data
summary_table <- result$summary_table

3. Using feature_selection()

selected_features <- feature_selection(
  file_path = "Demo/cleaned_data.xlsx",
  output_path = "Demo/selected_features.xlsx",
  fdr_threshold = 0.01
)

# View the selected features
print(selected_features)

4. Using predict_SDS()

predicted_sds <- predict_SDS(
  cleaned_data = cleaned_data,
  sf_test = selected_features,
  fixed_effects = c("Scan.date")
)

# View predictions
print(predicted_sds)

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

Dr. Medhat Mahmoud,
Statistical Scientist
Decision Pipeline & Analytics