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Data-driven crop growth simulation on time-varying generated images using multi-conditional generative adversarial

Lukas Drees1, Dereje T Demie2, Madhuri R Paul3

  • 1Remote Sensing Group, Institute of Geodesy and Geoinformation, University of Bonn, Niebuhrstr. 1a, Bonn, 53113, Germany. ldrees@uni-bonn.de.

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Summary
This summary is machine-generated.

This study introduces a novel framework for data-driven crop growth simulation, enhancing precision agriculture through realistic image generation and phenotyping. The model integrates multiple growth factors for accurate plant trait prediction.

Keywords:
Conditional GANCrop mixturesGrowth modelingImage generationMachine learning

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Image-based crop growth modeling aids precision agriculture by predicting plant traits like leaf area and biomass.
  • Accurate crop image generation requires integrating diverse growth conditions (initial stage, time, field treatment).
  • A research gap exists in comprehensively integrating various growth factors into image-based crop models.

Purpose of the Study:

  • To develop a flexible framework for data-driven crop growth simulation using image generation.
  • To enable realistic, time-varying artificial crop image generation based on multiple influencing factors.
  • To improve plant phenotyping accuracy and provide insights into growth-influencing conditions.

Main Methods:

  • A two-stage framework combining a conditional Wasserstein generative adversarial network (CWGAN) for image generation and a growth estimation model.
  • Conditional batch normalization (CBN) integrated into the CWGAN generator to incorporate diverse input conditions.
  • Evaluation of image quality using MS-SSIM, LPIPS, and FID metrics; plant phenotyping via trait derivation.

Main Results:

  • The framework generated realistic, sharp images across laboratory (Arabidopsis) and field (cauliflower, crop mixtures) datasets.
  • Incorporating treatment information (cultivars, sowing densities) improved generation quality and phenotyping accuracy (biomass estimation) in crop mixtures.
  • Adding process-based simulated biomass as a condition enhanced phenotypic trait accuracy, demonstrating potential as an interface between data-driven and process-based models.

Conclusions:

  • Multi-conditional CWGAN effectively generates realistic future plant appearances for crop growth simulation.
  • The framework complements process-based models by overcoming limitations like reliance on assumptions and low field-localization specificity.
  • Provides realistic spatial crop development visualizations, leading to high explainability of model predictions.