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Related Experiment Video

Updated: Sep 7, 2025

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects
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Depth image conversion model based on CycleGAN for growing tomato truss identification.

Dae-Hyun Jung1, Cheoul Young Kim2,3, Taek Sung Lee1

  • 1Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung-si, Gangwon-do, 25451, Republic of Korea.

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|June 17, 2022
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Summary
This summary is machine-generated.

A robot-based system using CycleGAN effectively identifies tomato growing trusses, crucial for early-stage growth control in greenhouses. This advanced imaging technology enhances precision agriculture and crop management.

Keywords:
Convolutional neural networkDeep learningGenerative adversarial networksRobot platform

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Area of Science:

  • Agricultural Robotics
  • Computer Vision in Agriculture
  • Plant Science

Background:

  • The growing truss is a critical stem region in tomato plants, sensitive to environmental conditions and vital for early-stage growth monitoring.
  • Robotic systems integrated with AI and image processing offer advanced methods for real-time data acquisition and analysis in agriculture.
  • CycleGAN, a generative adversarial network, excels at transforming unpaired images, proving useful for specialized image generation and analysis.

Purpose of the Study:

  • To develop and evaluate a robot-based system for simultaneous RGB and depth image acquisition of tomato plant growing trusses.
  • To assess the efficacy of the CycleGAN algorithm in segmenting and extracting growth information from these images.
  • To confirm the on-site feasibility of CycleGAN-enhanced image extraction for greenhouse applications.

Main Methods:

  • A robot-based system was engineered to capture synchronized RGB and depth images of tomato growing trusses.
  • Image processing techniques were employed to locate and extract growth information from the acquired images.
  • The CycleGAN algorithm was utilized for image transformation and segmentation, with performance evaluated using false negative (FN), false positive (FP), and mean intersection over union (mIoU) metrics.

Main Results:

  • The CycleGAN algorithm achieved a mean intersection over union (mIoU) of 69.25% ± 4.42% in segmenting tomato growing trusses, outperforming standard depth image processing (63.56% ± 8.44%).
  • FN and FP values for CycleGAN segmentation were 19.24% ± 1.45% and 18.24% ± 1.54%, respectively, demonstrating high precision.
  • The study confirmed the practical application of CycleGAN for image extraction by a scanning robot in a greenhouse environment.

Conclusions:

  • The CycleGAN algorithm demonstrates high precision in identifying tomato growing trusses, making it a valuable tool for agricultural image analysis.
  • The robot-based system integrated with CycleGAN is suitable for on-site image extraction in commercial tomato greenhouses.
  • This approach is poised to advance vision technology for unmanned robotic platforms in monitoring plant growth indicators within greenhouses.