AIMC Topic: Plants

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Machine learning in plant-pathogen interactions: empowering biological predictions from field scale to genome scale.

The New phytologist Mar 26, 2019
Machine learning (ML) encompasses statistical methods that learn to identify patterns in complex datasets. Here, I review application areas in plant-pathogen interactions that have recently benefited from ML, such as disease monitoring, the discovery...
Gene Regulatory Networks Genomics Plants Machine Learning Power, Psychological
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Sharing the Right Data Right: A Symbiosis with Machine Learning.

Trends in plant science Nov 26, 2018
In 2014 plant phenotyping research was not benefiting from the machine learning (ML) revolution because appropriate data were lacking. We report the success of the first open-access dataset suitable for ML in image-based plant phenotyping suitable fo...
Plants Machine Learning Symbiosis Phenotype
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Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models.

BMC genomics Nov 26, 2018
BACKGROUND: An increasing number of studies reported that exogenous miRNAs (xenomiRs) can be detected in animal bodies, however, some others reported negative results. Some attributed this divergence to the selective absorption of plant-derived xenom...
High-Throughput Nucleotide Sequencing Databases, Nucleic Acid RNA, Plant MicroRNAs Plants Neural Networks, Computer Biomarkers Humans
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Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting.

The Plant journal : for cell and molecular biology Sep 11, 2018
Direct observation of morphological plant traits is tedious and a bottleneck for high-throughput phenotyping. Hence, interest in image-based analysis is increasing, with the requirement for software that can reliably extract plant traits, such as lea...
Software Phenotype Deep Learning Machine Learning Plants Image Processing, Computer-Assisted Plant Leaves
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A deep convolutional neural network approach for predicting phenotypes from genotypes.

Planta Aug 12, 2018
Deep learning is a promising technology to accurately select individuals with high phenotypic values based on genotypic data. Genomic selection (GS) is a promising breeding strategy by which the phenotypes of plant individuals are usually predicted b...
Machine Learning Genome-Wide Association Study Neural Networks, Computer Genetic Association Studies Models, Genetic Plants Selection, Genetic
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A heuristic method for fast and accurate phasing and imputation of single-nucleotide polymorphism data in bi-parental plant populations.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Aug 4, 2018
Key message New fast and accurate method for phasing and imputation of SNP chip genotypes within diploid bi-parental plant populations. This paper presents a new heuristic method for phasing and imputation of genomic data in diploid plant species. Ou...
Alleles Haplotypes Computer Heuristics Genetic Markers Plants Software Computer Simulation Polymorphism, Single Nucleotide Genotype Genomics Plant Breeding
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Deep Learning for Plant Species Classification Using Leaf Vein Morphometric.

IEEE/ACM transactions on computational biology and bioinformatics Jun 19, 2018
An automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. In this research, a new...
Algorithms Computational Biology Deep Learning Support Vector Machine Image Processing, Computer-Assisted Plant Leaves Bayes Theorem Plants
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Ecological Vulnerability Assessment Based on Fuzzy Analytical Method and Analytic Hierarchy Process in Yellow River Delta.

International journal of environmental research and public health Apr 25, 2018
The Yellow River Delta (YRD), located in Yellow River estuary, is characterized by rich ecological system types, and provides habitats or migration stations for wild birds, all of which makes the delta an ecological barrier or ecotone for inland area...
Models, Theoretical Ecosystem Rivers Plants China Environmental Monitoring Conservation of Natural Resources Weather Humans Fuzzy Logic Soil
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Machine learning modeling of plant phenology based on coupling satellite and gridded meteorological dataset.

International journal of biometeorology Apr 11, 2018
Changes in the timing of plant phenological phases are important proxies in contemporary climate research. However, most of the commonly used traditional phenological observations do not give any coherent spatial information. While consistent spatial...
Climate Plant Physiological Phenomena Meteorology Plant Development Poland Plants Machine Learning
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Integrating river hydromorphology and water quality into ecological status modelling by artificial neural networks.

Water research Apr 7, 2018
The aim of the study was to develop predictive models of the ecological status of rivers by using artificial neural networks. The relationships between five macrophyte indices and the combined impact of water pollution as well as hydromorphological d...
Neural Networks, Computer Rivers Ecosystem Models, Theoretical Plants Water Pollution Environmental Monitoring Water Quality Aquatic Organisms
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