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Related Experiment Video

Updated: May 27, 2025

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

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

Published on: January 21, 2013

Bean leaf image dataset annotated with leaf dimensions, segmentation masks, and camera calibration.

Karla Gabriele Florentino da Silva^1,2, Paulo Victor de Magalhães Rozatto¹, Kaio de Oliveira E Sousa¹

¹Department of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, MG, 36036-900, Brazil.

|February 17, 2025

Summary

This summary is machine-generated.

This study introduces a new dataset of common bean leaf images for plant science research. The dataset aids in developing computer vision models for leaf dimensioning, crucial for understanding plant responses.

Keywords:

Area estimation Deep learning Fiducial marker Leaf measurement Semantic segmentation

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Area of Science:

Plant Physiology
Computer Vision
Agricultural Science

Background:

Leaf dimensioning is critical for assessing plant health and responses to environmental factors like water availability, soil fertility, and pesticide exposure.
Accurate leaf measurements are essential for agricultural research and developing predictive models for crop yield and stress.

Purpose of the Study:

To present a comprehensive dataset of common bean (Phaseolus vulgaris) leaf images with detailed annotations for research purposes.
To facilitate the development of advanced deep learning algorithms for precise leaf dimensioning and related analyses in plant science.

Main Methods:

A dataset of 6981 images of 612 common bean leaves was created, with each leaf imaged alongside a fiducial marker.
Annotations include leaf dimensions (area, perimeter, length, width), image segmentation, marker pose, and camera calibration details.
Data collection involved controlled capture conditions to ensure consistency and usability for algorithm development.

Main Results:

The dataset provides meticulously annotated leaf dimensions and image properties for 612 individual common bean leaves.
Includes detailed information on image segmentation, marker area, marker pose, capture conditions, and camera calibration.

Conclusions:

The presented dataset is a valuable resource for researchers in computer vision and plant physiology.
It enables the advancement of deep learning models for leaf dimensioning, contributing to improved plant monitoring and agricultural practices.