Deep semi-supervised learning identifies tree species from natural images.
problem Identifying tree species in natural settings with limited labeled data.
method Two-fold approach using deep semi-supervised learning.
result Achieves 94.04% top-5 accuracy for leaves and 83.04% for bark.
Interpretable neural network for plant traits and species identification.
problem Plant phenotyping and identification.
method Neural network trained on UPWINS spectral library, with visualization of weights for trait-based spectral features.
result 90% accuracy in species identification with interpretable neural network.
Paper improves gas species identification in complex mixtures using neural networks.
problem Identifying gas species in multi-gas mixtures with high accuracy.
method Multi-label neural networks with optimal thresholding for IR spectroscopy.
result Optimal thresholding improves classification performance over conventional methods.
Phytoplankton plays an important role in marine ecosystem. It is defined as a biological factor to assess marine quality. The identification of phytoplankton species has a high potential for monitoring environmental, climate changes and for evaluating water quality. However, phytoplankton species identification is not …
Deep learning identifies frog species and detects new ones.
problem Identifying and detecting new species in morphologically similar groups.
method Machine learning, specifically deep neural networks, applied to frog hind limb skin texture.
result Deep neural networks can classify images into known species and new classes.
Image classification system identifies bumble bee species from images.
problem Manual identification of bumble bee species is time-consuming and requires expert knowledge.
method Transfer learning using Inception, VGG16, VGG19, and ResNet models.
result Inception and VGG classifiers achieved up to 23% accuracy for single species identification.
Microbial identification is a central issue in microbiology, in particular in the fields of infectious diseases diagnosis and industrial quality control. The concept of species is tightly linked to the concept of biological and clinical classification where the proximity between species is generally measured in terms o…
In general, object identification tends not to work well on ambiguous, amorphous objects such as vegetation. In this study, we developed a simple but effective approach to identify ambiguous objects and applied the method to several moss species. As a result, the model correctly classified test images with accuracy mor…
Machine learning methods have found many applications in Raman spectroscopy, especially for the identification of chemical species. However, almost all of these methods require non-trivial preprocessing such as baseline correction and/or PCA as an essential step. Here we describe our unified solution for the identifica…
Deep CNNs struggle with rare taxa; one-class classifiers help identify them.
problem Efficiently identifying rare benthic macroinvertebrates in insect monitoring.
method Combining deep CNNs with one-class classifiers to identify rare taxa.
result The proposed approach can improve rare species identification, supporting automation.
Machine learning identifies species by voice in remote areas.
problem Continuous monitoring of endangered species in remote areas.
method Training machine learning models on audio data to recognize species.
result Machine learning can accurately classify and recognize various species sounds.
SLIC-UAV monitors forest recovery using UAVs and machine learning.
problem Challenges in monitoring forest recovery, especially in logged tropical forests.
method Novel pipeline for UAV imagery analysis, combining crown labelling, species classification, and superpixel segmentation.
result SLIC-UAV achieves high accuracy in species mapping, from 79.3% to 90.5%.
Deep learning identifies 30 common bacterial pathogens from Raman spectra.
problem Rapid and accurate identification of pathogenic bacteria.
method State-of-the-art deep learning applied to large Raman spectroscopy dataset.
result 99.0% accuracy in identifying 30 common bacterial pathogens.
Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and minute traits in applicable fields of biological sciences, is scarce. Here we consider one such real world example viz., accurate identific…
Automated system identifies and counts insects from images.
problem Manual sorting and identification of insect samples is time-consuming and limits biodiversity mapping.
method Robot-enabled image-based identification machine using CNNs.
result Classification accuracy of 0.980 for initial dataset. New method uses UAV imagery and ML to map crops and weeds.
problem Mapping crops and weeds at high resolution.
method Machine Learning algorithms trained on expert-masked images.
result Maps with >90% identification efficiency at 5m altitude.
Deep learning system speeds up wildlife species identification from camera trap images.
problem Manual review of camera trap images is slow and resource-intensive.
method Combines machine and human intelligence for active learning.
result Matches state-of-the-art accuracy with minimal manual labels.
We evaluated the effectiveness of an automated bird sound identification system in a situation that emulates a realistic, typical application. We trained classification algorithms on a crowd-sourced collection of bird audio recording data and restricted our training methods to be completely free of manual intervention.…
Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that species are not independently distributed. We propose Deep Multi-Speci…
Framework uncovers symmetric and asymmetric species associations from data.
problem Retrieving bidirectional species associations from co-occurrence data.
method Machine learning framework modeling latent embeddings and joint generative model.
result Framework successfully recovers known symmetric and asymmetric associations.
Heterogeneous GNN improves species distribution modeling.
problem Predicting species occurrences and habitat suitability using environmental factors.
method Graph Neural Networks (GNN) for presence-only species distribution modeling.
result Heterogeneous GNN model outperforms single-species SDMs and baseline models.
GeoLifeCLEF 2020 dataset pairs species observations with environmental data.
problem Understanding geographic species distribution.
method Presented a dataset of species observations with environmental features.
result Advances in location-based species recommendation.
SDSR reconstructs species trees from genetic markers efficiently.
problem Challenges in reconstructing species trees from genetic data.
method Spectral divide-and-conquer approach based on graph theory.
result SDSR achieves up to 10-fold faster runtime with comparable accuracy.
Automated malaria diagnosis from field slides achieves accurate results.
problem Challenges in analyzing field-prepared thin blood film microscopy images.
method Fully automated framework using machine learning, including CNNs trained on diverse field samples.
result Results are close to sufficient for drug resistance monitoring and clinical use-cases.
Forest tree species mapped with high accuracy using satellite data.
problem Classifying dominant tree species in Swedish forests.
method Extreme gradient boosting model with Bayesian optimization, combining Sentinel-1/2 satellite data and field observations.
result Overall accuracy of 85%, F1 score of 0.82, Matthews correlation coefficient of 0.81.
StatEcoNet models species distribution using neural networks to correct observation errors.
problem Correcting observation errors in wildlife surveys for accurate species distribution modeling.
method StatEcoNet integrates a graphical generative model with neural networks to address SDM challenges.
result StatEcoNet outperforms traditional methods on simulated and real datasets.
Gaussian process framework learns interaction kernels in multi-species particle systems.
problem Learning interaction kernels in multi-species interacting particle systems from trajectory data.
method Nonparametric Bayesian approach with Gaussian processes.
result Established rigorous statistical guarantees for recoverability and optimality of interaction kernels.
Improved chemical predictions through compressed atomic species representations.
problem Intractable chemical space of molecules and materials.
method Introducing elemental modes for compressed representation of atomic species.
result Elemental modes enable improvements in machine learning tasks for chemical predictions.
Machine learning improves predicting species interactions based on traits.
problem Variability in empirical trait-matching studies for ecological networks.
method Compared conventional GLMs with ML models (Random Forest, Boosted Regression Trees, etc.) on simulated and real data.
result ML models outperform GLMs in predicting species interactions and identifying trait-matching combinations.
PHIBP models complex microbiome data with shared parameters.
problem Complex, sparse count data in microbiome analysis.
method Bayesian nonparametric framework with shared species parameters.
result Flexible multivariate count model with tractable inference.
AMP0 predicts antimicrobial peptides targeting specific microbes.
problem Low-throughput screening of antimicrobial peptides.
method Zero-shot and few-shot machine learning.
result AMP0 can predict antimicrobial activity against specific microbes.
PLIT identifies plant lncRNAs from RNA-seq data with high accuracy.
problem Inaccurate identification of lncRNAs in plant transcriptomic datasets.
method PLIT uses L1 regularization and iRF classification to select optimal features from sequence and codon-bias data.
result PLIT outperforms existing CPC tools in identifying lncRNAs in plant RNA-seq datasets.
Deep model tackles zero-inflated multi-species abundance estimation.
problem Predicting species distribution across landscapes with inflated zero counts.
method Proposes a novel deep learning model combining multivariate probit and log-normal distributions.
result Model outperforms existing methods on bird and fish population datasets.
DeepMaxent uses neural networks to improve species distribution models.
problem Sampling biases and lack of absence data in presence-only observations.
method DeepMaxent employs neural networks to learn shared features among species using the maximum entropy principle.
result DeepMaxent outperforms traditional methods in predicting species distributions, especially in unevenly sampled regions.
We consider the problem of estimating the evolutionary history of a set of species (phylogeny or species tree) from several genes. It is known that the evolutionary history of individual genes (gene trees) might be topologically distinct from each other and from the underlying species tree, possibly confounding phyloge…
Method learns cell interaction rules from individual trajectories.
problem Inferring interaction rules from heterogeneous cellular data.
method WSINDy for second order IPSs, learning individual cell models.
result Efficiently identifies different species and best-fit models for each.
Bayesian method improves estimation of unseen species.
problem Estimating unseen species in biological and physical sciences.
method Bayesian nonparametric approach with Pitman-Yor prior and Gaussian credible intervals.
result Improves asymptotic credible intervals for unseen species estimation.
The paper uses VGG-19 for plant species classification from leaf images.
problem Manual inspection of plant species by botanists is time-consuming.
method Transfer learning with VGG-19 for feature extraction and classification.
result The model achieves 99.70% accuracy in predicting plant species.
Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for croplands, ignoring the weed management problems facing rangeland stock farmers. Perhaps the greatest obstacle to widespread uptake of robotic weed con…
Populations of species in ecosystems are often constrained by availability of resources within their environment. In effect this means that a growth of one population, needs to be balanced by comparable reduction in populations of others. In neutral models of biodiversity all populations are assumed to change increment…
Most animals possess the ability to actuate a vast diversity of movements, ostensibly constrained only by morphology and physics. In practice, however, a frequent assumption in behavioral science is that most of an animal's activities can be described in terms of a small set of stereotyped motifs. Here we introduce a m…
Avian Influenza breakouts cause millions of dollars in damage each year globally, especially in Asian countries such as China and South Korea. The impact magnitude of a breakout directly correlates to time required to fully understand the influenza virus, particularly the interspecies pathogenicity. The procedure requi…
Compact models for methane/air combustion reduce complexity without sacrificing accuracy.
problem Creating accurate, computationally efficient models for methane combustion.
method Data-oriented three-step methodology: 1) Remove non-essential species, 2) Numerically optimize to key species profiles, 3) Machine learning to refine parameters.
result Produced 19 and 15 species compact models that outperform current state-of-the-art models in accuracy and range of conditions.
Translating potential disease biomarkers between multi-species 'omics' experiments is a new direction in biomedical research. The existing methods are limited to simple experimental setups such as basic healthy-diseased comparisons. Most of these methods also require an a priori matching of the variables (e.g., genes o…
ConvNet classifies whale vocalizations and ambient noise in acoustic recordings.
problem Automated detection and classification of marine mammal vocalizations in acoustic recordings.
method Convolutional Neural Network with a novel acoustic representation.
result Classifier accurately detects and classifies whale vocalizations and ambient noise.
Deep learning improves tree species classification accuracy in imbalanced contexts.
problem Inaccurate tree species maps for large areas, especially in imbalanced contexts.
method Used deep learning models (including convolutional and attention-based) on Sentinel-2 multispectral satellite time series data.
result Deep learning models achieve higher accuracy and F1-macro scores compared to Random Forest in imbalanced contexts.
The technique of Formal Concept Analysis is applied to a dataset describing the traits of rodents, with the goal of identifying zoonotic disease carriers,or those species carrying infections that can spillover to cause human disease. The concepts identified among these species together provide rules-of-thumb about the …
Estimating the number of unseen species is an important problem in many scientific endeavors. Its most popular formulation, introduced by Fisher, uses n samples to predict the number U of hitherto unseen species that would be observed if t⋅n new samples were collected. Of considerable interest is the largest…