PlantSpecLab: A comprehensive open-source platform for high-throughput plant spectral data processing and phenotypic...

Area of Science:

• Agricultural Science
• Plant Biology
• Data Science

Background:

• High-throughput plant phenotyping using hyperspectral imaging (HSI) is crucial for crop improvement and global food security.
• Current HSI data processing faces bottlenecks, with limited options between expensive commercial software and complex open-source libraries.
• A need exists for accessible, efficient tools to bridge the gap in HSI data analysis for researchers.

Purpose of the Study:

• To develop PlantSpecLab, an open-source, no-code platform unifying the HSI workflow from image processing to modeling.
• To introduce novel spectrally guided segmentation and a Fractional-Order Differencing (FOD) preprocessor for enhanced feature extraction.
• To reduce the technical barrier for HSI analysis, enabling faster crop improvement.

Main Methods:

• Developed PlantSpecLab, an integrated, no-code platform for HSI data analysis.
• Implemented spectrally guided segmentation (Range Averaging, Difference Enhancement) and Fractional-Order Differencing (FOD) preprocessing.
• Validated the platform on diverse in-house and public datasets for plant phenotyping tasks.

Main Results:

• FOD-preprocessed spectra significantly improved model performance compared to conventional methods.
• Achieved 82.86% accuracy for tomato maturity classification and an average R² of 0.8638 for fruit firmness.
• PlantSpecLab matched commercial software accuracy while reducing workflow time by over 90%.

Conclusions:

• PlantSpecLab offers a transparent and efficient solution for HSI data analysis, lowering technical barriers.
• The platform enables researchers to focus on biological interpretation rather than complex computation.
• Accelerated HSI analysis through PlantSpecLab can significantly contribute to crop improvement efforts.