Batch-Mask: Automated image segmentation for organisms with limbless or non-standard body forms

From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2

Premise

Quantitative plant traits play a crucial role in biological research. However, traditional methods for measuring plant morphology are time consuming and have limited scalability. We present LeafMachine2, a suite of modular machine learning and computer vision tools that can automatically extract a base set of leaf traits from digital plant data sets.

Methods

LeafMachine2 was trained on 494,766 manually prepared annotations from 5648 herbarium images obtained from 288 institutions and representing 2663 species; it employs a set of plant component detection and segmentation algorithms to isolate individual leaves, petioles, fruits, flowers, wood samples, buds, and roots. Our landmarking network automatically identifies and measures nine pseudo-landmarks that occur on most broadleaf taxa. Text labels and barcodes are automatically identified by an archival component detector and are prepared for optical character recognition methods or natural language processing algorithms.

Results

LeafMachine2 can extract trait data from at least 245 angiosperm families and calculate pixel-to-metric conversion factors for 26 commonly used ruler types.

Discussion

LeafMachine2 is a highly efficient tool for generating large quantities of plant trait data, even from occluded or overlapping leaves, field images, and non-archival data sets. Our project, along with similar initiatives, has made significant progress in removing the bottleneck in plant trait data acquisition from herbarium specimens and shifted the focus toward the crucial task of data revision and quality control.

Open Source Solutions in Experimental Design: An Introduction to the Symposium

The Open Science movement has increased dramatically in popularity with deserved calls to action around transparency, access to resources, and inclusion in our field. However, its practical applications within experimental design have been slow to uptake, with researchers unsure where to even start with the dizzying array of open source hardware and software solutions available. The perceived time investment and unknown cost, especially in implementing open source hardware, has stagnated the implementation of inexpensive experimental solutions, but we sought to increase awareness to lower the barrier to participation in this space. While there are countless technical and financial advantages to integrating open source solutions into every biologist's experimental design, we put an emphasis on the "people" part of the equation in our symposium. This symposium championed innovative experimental designs by early career SICB researchers across all fields of biology, from plants to animals, in the lab or in the field, or even virtually engaging with the public and students. The open science movement operates within community norms that champion transparency, continuous development, and collaboration. These values are congruent with the priorities of reducing barriers to participation in science, and we hope our symposium's collection of open source solutions encourages readers to adopt these or other innovative designs into their own experimentation.