I-Nema: A Biological Image Dataset for Nematode Recognition

• URL: http://arxiv.org/abs/2103.08335v1
• Date: Mon, 15 Mar 2021 12:29:37 GMT
• Title: I-Nema: A Biological Image Dataset for Nematode Recognition
• Authors: Xuequan Lu, Yihao Wang, Sheldon Fung, and Xue Qing
• Abstract summary: Nematode worms are one of most abundant metazoan groups on the earth, occupying diverse ecological niches. Accurate recognition or identification of nematodes are of great importance for pest control, soil ecology, bio-geography, habitat conservation and against climate changes. Computer vision and image processing have witnessed a few successes in species recognition of nematodes; however, it is still in great demand.
• Score: 3.1918817988202606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nematode worms are one of most abundant metazoan groups on the earth, occupying diverse ecological niches. Accurate recognition or identification of nematodes are of great importance for pest control, soil ecology, bio-geography, habitat conservation and against climate changes. Computer vision and image processing have witnessed a few successes in species recognition of nematodes; however, it is still in great demand. In this paper, we identify two main bottlenecks: (1) the lack of a publicly available imaging dataset for diverse species of nematodes (especially the species only found in natural environment) which requires considerable human resources in field work and experts in taxonomy, and (2) the lack of a standard benchmark of state-of-the-art deep learning techniques on this dataset which demands the discipline background in computer science. With these in mind, we propose an image dataset consisting of diverse nematodes (both laboratory cultured and naturally isolated), which, to our knowledge, is the first time in the community. We further set up a species recognition benchmark by employing state-of-the-art deep learning networks on this dataset. We discuss the experimental results, compare the recognition accuracy of different networks, and show the challenges of our dataset. We make our dataset publicly available at: https://github.com/xuequanlu/I-Nema

Related papers

• Insect Identification in the Wild: The AMI Dataset [35.41544843896443]
  Insects represent half of all global biodiversity, yet many of the world's insects are disappearing. Despite this crisis, data on insect diversity and abundance remain woefully inadequate. We provide the first large-scale machine learning benchmarks for fine-grained insect recognition.
  arXiv  Detail & Related papers  (2024-06-18T09:57:02Z)
• Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data [5.943822554753426]
  Deep neural networks can successfully automate the classification of an image into a known species group for which it has been trained. We demonstrate that the algorithm can successfully classify an image into a new class if the image does not belong to the existing classes.
  arXiv  Detail & Related papers  (2023-11-15T02:57:59Z)
• SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data [68.2366021016172]
  We present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird. We also provide a dataset in Kenya representing low-data regimes. We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks.
  arXiv  Detail & Related papers  (2023-11-02T02:00:27Z)
• Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition [30.327642724046903]
  Species196 is a large-scale semi-supervised dataset of 196-category invasive species. It collects over 19K images with expert-level accurate annotations Species196-L, and 1.2M unlabeled images of invasive species Species196-U.
  arXiv  Detail & Related papers  (2023-09-25T14:46:01Z)
• Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
  Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets. We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
  arXiv  Detail & Related papers  (2023-06-05T03:36:01Z)
• Bugs in the Data: How ImageNet Misrepresents Biodiversity [98.98950914663813]
  We analyze the 13,450 images from 269 classes that represent wild animals in the ImageNet-1k validation set. We find that many of the classes are ill-defined or overlapping, and that 12% of the images are incorrectly labeled. We also find that both the wildlife-related labels and images included in ImageNet-1k present significant geographical and cultural biases.
  arXiv  Detail & Related papers  (2022-08-24T17:55:48Z)
• Florida Wildlife Camera Trap Dataset [48.99466876948454]
  We introduce a challenging wildlife camera trap classification dataset collected from two different locations in Southwestern Florida. The dataset consists of 104,495 images featuring visually similar species, varying illumination conditions, skewed class distribution, and including samples of endangered species.
  arXiv  Detail & Related papers  (2021-06-23T18:53:15Z)
• Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection [60.83360138070649]
  The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
  arXiv  Detail & Related papers  (2021-06-14T21:57:40Z)
• Unifying data for fine-grained visual species classification [15.14767769034929]
  We present an initial deep convolutional neural network model, trained on 2.9M images across 465 fine-grained species. The long-term goal is to enable scientists to make conservation recommendations from near real-time analysis of species abundance and population health.
  arXiv  Detail & Related papers  (2020-09-24T01:04:18Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.