AIMC Topic: Plants

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Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment.

PLoS computational biology Apr 19, 2021
Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp ho...
Neural Networks, Computer Biodiversity France Models, Statistical Plants
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A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data.

PloS one Mar 31, 2021
The interpretation of archaeological features often requires a combined methodological approach in order to make the most of the material record, particularly from sites where this may be limited. In practice, this requires the consultation of differ...
Machine Learning Archaeology Geology Bayes Theorem Plants Algorithms Soil
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Integrating structure-based machine learning and co-evolution to investigate specificity in plant sesquiterpene synthases.

PLoS computational biology Mar 22, 2021
Sesquiterpene synthases (STSs) catalyze the formation of a large class of plant volatiles called sesquiterpenes. While thousands of putative STS sequences from diverse plant species are available, only a small number of them have been functionally ch...
Amino Acid Sequence Sesquiterpenes Evolution, Molecular Plants Substrate Specificity Sequence Homology, Amino Acid Protein Conformation Cations Machine Learning Alkyl and Aryl Transferases
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Deep learning-based high-throughput phenotyping can drive future discoveries in plant reproductive biology.

Plant reproduction Mar 16, 2021
Advances in deep learning are providing a powerful set of image analysis tools that are readily accessible for high-throughput phenotyping applications in plant reproductive biology. High-throughput phenotyping systems are becoming critical for answe...
Image Processing, Computer-Assisted Plants Biology Neural Networks, Computer Deep Learning
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1, 2, 3, GO! Venture beyond gene ontologies in plant evolutionary research.

American journal of botany Mar 8, 2021
Biological Evolution Biological Ontologies Gene Ontology Plants
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Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Jan 21, 2021
Instance segmentation is an important task for biomedical and biological image analysis. Due to the complicated background components, the high variability of object appearances, numerous overlapping objects, and ambiguous object boundaries, this tas...
Neural Networks, Computer Algorithms Image Processing, Computer-Assisted Diagnostic Imaging Humans Plants Semantics
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Multi-view classification with convolutional neural networks.

PloS one Jan 12, 2021
Humans' decision making process often relies on utilizing visual information from different views or perspectives. However, in machine-learning-based image classification we typically infer an object's class from just a single image showing an object...
Plants Neural Networks, Computer Image Processing, Computer-Assisted
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PCirc: random forest-based plant circRNA identification software.

BMC bioinformatics Jan 6, 2021
BACKGROUND: Circular RNA (circRNA) is a novel type of RNA with a closed-loop structure. Increasing numbers of circRNAs are being identified in plants and animals, and recent studies have shown that circRNAs play an important role in gene regulation. ...
RNA, Circular Plants Software Machine Learning Computational Biology
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Branching principles of animal and plant networks identified by combining extensive data, machine learning and modelling.

Journal of the Royal Society, Interface Jan 6, 2021
Branching in vascular networks and in overall organismic form is one of the most common and ancient features of multicellular plants, fungi and animals. By combining machine-learning techniques with new theory that relates vascular form to metabolic ...
Animals Plants Mathematics Machine Learning
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An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture.

PloS one Dec 17, 2020
A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such a...
Plant Development Plants Deep Learning Image Processing, Computer-Assisted Canada Agriculture Humans Machine Learning Algorithms
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