DOI: 10.5281/zenodo.18090524
title: Datasets for SALMA: A Machine Learning Tool for Precise Leaf Morphology Measurements of Small Leaves

description: Structure

Dataset for the software/paper "SALMA: A Machine Learning Tool for Precise Leaf Morphology Measurements of Small Leaves".

Please see the code repository for executables and code: https://github.com/lnilya/salma

 

This archive comprises three datasets based on the SALMA paper. Each dataset is contained in its own folder:

 

1. Main Dataset: This dataset is the primary dataset used in the paper, containing 3332 individual leaf images from 64 plant species in New Zealand, scanned at 600 dpi. Please see 

2. Low-Contrast Dataset: The dataset consists of digital photographs under various adverse lighting conditions (e.g., blurriness, varying contrast, blue or red colouration). It contains 130 leaves from four alpine species.

3. Diverse Morphology Dataset: Consists of 212 leaves from six species containing damage from dessication and herbivory, compound leaves, narrow-leaved grasses and leaf fragments. This dataset includes some species extracted from Both et al. 2020. 

 

If not otherwise indicated, all images have been obtained through field work.



Folder Structure





Leaf Sheets: Contains the raw scans of leaves and their human-delineated masks.



Subfolders correspond to species

Files are numbered consecutively

*.jpg files are scans

*.png (same file name as jpgs) are human-delineated masks.





Leaf Images: Single leaves extracted from the leaf sheets for easier evaluation. The results presented in our paper use these images.



Each file contains a crop of a single leaf from a sheet.

Sheet identity is ignored, and leaves are numbered consecutively.

If the rectangular crop of the leaf contains a part of another leaf, it is overlayed with a white mask.

*.jpg files are scans

*.png (same file name as jpgs) are human-delineated masks





Leaf Segmentations by Method: Contains the result of various algorithms on segmenting the individual leaves. The results presented in our paper are computed from these segmentations by comparing the contained *.png masks to the human-delineated masks in the Leaf Images folder. The subfolders are:



Otsu: Applies Otsu's threshold on the intensity channel.

Optimal: Picks an intensity threshold that most resembles the human-delineated mask output. This is not an automated algorithm, but an upper bound on any intensity-threshold-based algorithm (see our paper for discussion).

FAMeLeS: Output of the ImageJ plugin published by Montès et al. 2024

ColsGrad/SALMA: Output of SALMA algorithm using colour and gradient information. This folder also contains a *.pickle file with the trained model for that species (and resolution). The number of leaves used for training varies between 1 and 4; the best-performing model and its results are contained in this folder.

ColsGrad/SALMA_1..4: Output of SALMA algorithm using colour and gradient information and using 1,2,3 or 4 leaves for training, respectively. (This can be used to compare how SALMA's performance reacts to a varying number of leaves used for training the model.)





Leaf Sheets - Corrected: Only applies to the Low-contrast dataset. This folder contains the leaf sheets after white balance correction using the ImageJ plugin from Mascalchi 2016. In the low-contrast dataset, these corrected images are used for subsequent steps (i.e., segmentation).



 

Acknowledgements

We acknowledge Rachael Lockhart, Olivia Bird, Indira Leon Garcia and Janelle Veenendaal for contributing data. We are grateful to Moses Njau, Vaustine Obura, and Job Munene for segmentation of the images.