Automating identification and morphometric measurement of soil fauna with machine learning

No abstract is available for this paper.

• Publication year

  2026

• Venue

  Soil Ecology Letters

• Publication date

  2026-01-20

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1007/s42832-026-0396-5
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Variations in body size and reproductive mode of oribatid mites along an altitudinal gradient in a temperate mountain region

  2025cited by this paper
• Automatic identification of Collembola with deep learning techniques

  2024influential reference
• Counting nematodes made easy: leveraging AI-powered automation for enhanced efficiency and precision

  2024cited by this paper
• PENet: A phenotype encoding network for automatic extraction and representation of morphological discriminative features

  2023influential reference
• Responses of soil fauna community under changing environmental conditions

  2023cited by this paper
• Survey of automatic plankton image recognition: challenges, existing solutions and future perspectives

  2023cited by this paper
• Responses of soil fauna communities to the individual and combined effects of multiple global change factors.

  2022cited by this paper
• CollembolAI, a macrophotography and computer vision workflow to digitize and characterize samples of soil invertebrate communities preserved in fluid

  2022influential reference
• A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation

  2021influential reference
• An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

  2020cited by this paper
• Soil functional biodiversity and biological quality under threat: intensive land use outweighs climate change.

  2020cited by this paper
• A survey on Image Data Augmentation for Deep Learning

  2019cited by this paper
• CBAM: Convolutional Block Attention Module

  2018cited by this paper
• Bland-Altman analysis: A paradigm to understand correlation and agreement

  2018cited by this paper
• Deep learning approach to bacterial colony classification

  2017cited by this paper
• U-Net: Convolutional Networks for Biomedical Image Segmentation

  2015cited by this paper
• Understanding Bland Altman analysis

  2015cited by this paper
• Productivity affects the density-body mass relationship of soil fauna communities.

  2014cited by this paper
• Recurrent Models of Visual Attention

  2014cited by this paper
• Consumer-resource body-size relationships in natural food webs.

  2006cited by this paper
• Recent advances in Collembola systematics

  2004cited by this paper
• Toward a metabolic theory of ecology

  2004cited by this paper
• Breadth-first search and its application to image processing problems

  2001cited by this paper
• Rehabilitation of gamma

  1998cited by this paper
• Soil function in a changing world: the role of invertebrate ecosystem engineers

  1997cited by this paper
• Morphological Area Openings and Closings for Grey-scale Images

  1994cited by this paper
• A concordance correlation coefficient to evaluate reproducibility.

  1989cited by this paper
• Principal component analysis

  1987cited by this paper
• A fast parallel algorithm for thinning digital patterns

  1984cited by this paper

• No citing papers are available for this paper.