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Related Experiment Video

Updated: May 24, 2025

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

Novel augmentation techniques using diffusion models for green wall plant health classification.

MinSeok Yoon¹, Younghoon Lee²

¹Department of Data Science, Seoul National University of Science and Technology, 232, Gongneung-ro, Nowon-gu, Seoul, 01811, Republic of Korea.

Computers in Biology and Medicine

|March 5, 2025

Summary

This summary is machine-generated.

This study introduces a new method using Diffusion Models to create synthetic "Slightly Wilted" plant data. This improves the accuracy of green wall plant health monitoring systems.

Keywords:

Diffusion models Green walls Image augmentation Image classification Plant health classification

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Area of Science:

Botany and Horticulture
Computer Science and Artificial Intelligence
Environmental Science

Background:

Green walls offer environmental benefits but require diligent plant health monitoring.
Current deep learning models struggle with data scarcity for critical plant states like 'Slightly Wilted'.
Labeling ambiguities in continuous plant deterioration hinder accurate classification.

Purpose of the Study:

To develop an innovative data augmentation technique for improving plant health classification in green walls.
To address the challenge of data scarcity for the 'Slightly Wilted' plant state.
To enhance the precision and actionability of green wall maintenance insights.

Main Methods:

Utilized Diffusion Models to synthetically generate 'Slightly Wilted' plant data.
Interpolated between 'Normal' and 'Wilted' plant states to create diverse synthetic samples.
Assigned soft labels based on synthesis ratios to improve classification model training.

Main Results:

The proposed augmentation method significantly improved classification accuracy and F1 score by up to 4% compared to baseline models.
Demonstrated enhanced performance over models initialized with ImageNet weights.
Showcased the ability to provide a more granular assessment of plant health severity.

Conclusions:

The Diffusion Model-based augmentation effectively addresses data scarcity for 'Slightly Wilted' plants.
The approach enhances the performance of plant health classification models for green wall maintenance.
This method offers more precise and actionable insights for maintaining green wall systems.