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A method for phenotyping lettuce volume and structure from 3D images.

Victor Bloch1,2, Alexey Shapiguzov3, Titta Kotilainen3

  • 1Production Systems Unit, Natural Resources, Institute Luke (Finland), 00790, Helsinki, Finland. victor.bloch@volcani.agri.gov.il.

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|February 25, 2025
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Summary

A new method using plant geometric features accurately estimates lettuce fresh weight and rosette structure. This approach offers improved detail and addresses limitations of current deep learning models for crop management.

Keywords:
Lettuce 3D modellingLettuce fresh weightPlant rosette structure

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Area of Science:

  • Agricultural Engineering
  • Plant Science
  • Computer Vision

Background:

  • Accurate plant growth monitoring is vital for crop management.
  • RGBD cameras offer a non-invasive method for modeling plants like lettuce.
  • Current deep learning models for fresh weight estimation lack domain adaptation and dataset availability.

Purpose of the Study:

  • To develop a novel method for estimating lettuce rosette structure and volume using plant geometric features.
  • To compare the proposed method with existing surface reconstruction techniques.
  • To improve the accuracy of fresh weight estimation in lettuce.

Main Methods:

  • A new method was developed to create a tight hull around plant point clouds, preserving rosette detail and filling surface holes.
  • The proposed method was compared against Ball Pivoting and Alpha Shapes for surface reconstruction.
  • Linear regression was used to estimate fresh weight based on plant volume and geometric features.
  • A dataset of 402 lettuce plant point clouds was created using multi-view 3D cameras.

Main Results:

  • The proposed method effectively models lettuce rosette structure and volume.
  • Fresh weight estimation achieved a root mean square error (RMSE) of 18.2 g using plant volume alone.
  • Including geometric features improved RMSE to 17.3 g.
  • New geometric features characterizing leaf density were introduced.

Conclusions:

  • Plant geometric features provide a robust method for lettuce growth modeling and fresh weight estimation.
  • The developed approach overcomes limitations of existing deep learning models.
  • The introduced geometric features have potential applications in plant breeding.