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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Related Experiment Video

Updated: Jul 16, 2025

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

Local refinement mechanism for improved plant leaf segmentation in cluttered backgrounds.

Ruihan Ma^1,2, Alvaro Fuentes^1,2, Sook Yoon³

¹Department of Electronics Engineering, Jeonbuk National University, Jeonbuk, Republic of Korea.

Frontiers in Plant Science

|September 15, 2023

Summary

This study introduces a deep learning method for accurate plant leaf segmentation in challenging field conditions. The approach enhances image processing for better phenotyping and smart agriculture applications.

Keywords:

cluttered background deep learning filtering leaf instance segmentation plant phenotyping

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LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

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Published on: January 21, 2013

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LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

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Published on: January 21, 2013

Area of Science:

Agricultural Science
Computer Vision
Plant Biology

Background:

Plant phenotyping is vital for understanding crop growth and requires accurate data collection.
Current image-based phenotyping methods struggle with real-world field conditions like image blurring and occlusion.
Automated leaf segmentation is crucial for deriving phenotypic traits like leaf count and size.

Purpose of the Study:

To develop a deep learning-based leaf instance segmentation method for improved plant phenotyping.
To enhance segmentation performance in cluttered agricultural environments using a local refinement mechanism.
To enable automatic recognition of phenotypic data for smart agriculture.

Main Methods:

A deep learning architecture for leaf instance segmentation was employed.
A local refinement mechanism utilizing Gaussian low-pass and High-boost filters was integrated.
The refinement mechanism was tested on training and testing datasets with varying kernel sizes.
Experiments were conducted on a tomato leaf dataset.

Main Results:

The proposed method accurately segmented tomato leaves in complex backgrounds.
The High-boost filter with larger kernel sizes increased the number of detected leaf instances.
Larger Gaussian low-pass filter kernel sizes negatively impacted prediction performance.
The system successfully facilitated the derivation of phenotypic information.

Conclusions:

The deep learning approach with local refinement effectively addresses challenges in real-world plant phenotyping.
This method enables automatic recognition of phenotypic data, supporting smart agriculture initiatives.
The findings contribute to improving crop yield and agricultural productivity through enhanced phenotyping.