AIMC Topic: Oryza

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Accurate prediction of species-specific 2-hydroxyisobutyrylation sites based on machine learning frameworks.

Analytical biochemistry May 28, 2020
Lysine 2-hydroxyisobutyrylation (K) is a newly discovered post-translational modification (PTM) across eukaryotes and prokaryotes in recent years, which plays a significant role in diverse cellular functions. Accurate prediction of K sites is a first...
Seeds Protein Processing, Post-Translational Oryza Humans Machine Learning Species Specificity Lysine Hydroxybutyrates Bryopsida Saccharomyces cerevisiae HeLa Cells
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SemanticGO: a tool for gene functional similarity analysis in Arabidopsis thaliana and rice.

Plant science : an international journal of experimental plant biology May 23, 2020
Gene or pathway functional similarities are important information for researchers. However, these similarities are often described sparsely and qualitatively. The latent semantic analysis of Arabidopsis thaliana (Arabidopsis) Gene Ontology (GO) data ...
Databases, Genetic Genes, Plant Oryza Gene Regulatory Networks Natural Language Processing Genetic Vectors Arabidopsis Metabolic Networks and Pathways Semantics
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Detection of rice plant diseases based on deep transfer learning.

Journal of the science of food and agriculture Mar 14, 2020
BACKGROUND: As the primary food for nearly half of the world's population, rice is cultivated almost all over the world, especially in Asian countries. However, the farmers and planting experts have been facing many persistent agricultural challenges...
Deep Learning Image Processing, Computer-Assisted Oryza Neural Networks, Computer Plant Diseases
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Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Mar 6, 2020
The phenomena of rice adulteration and shoddy rice arise continuously in high-quality rice and reduce the interests of producers, consumers and traders. Hyperspectral imaging (HSI) was conducted to determine rice variety using a deep learning network...
Principal Component Analysis Oryza Hyperspectral Imaging China Image Processing, Computer-Assisted Entropy Geography Neural Networks, Computer Spectroscopy, Near-Infrared Deep Learning Algorithms
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A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network.

Sensors (Basel, Switzerland) Jan 21, 2020
Increasing grain production is essential to those areas where food is scarce. Increasing grain production by controlling crop diseases and pests in time should be effective. To construct video detection system for plant diseases and pests, and to bui...
Plant Diseases Deep Learning Oryza Video Recording Neural Networks, Computer Machine Learning
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Spectroscopy based novel spectral indices, PCA- and PLSR-coupled machine learning models for salinity stress phenotyping of rice.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Dec 23, 2019
Identification and development of salinity tolerant genotypes and varieties are one of the promising ways to improve productivity of salt-affected soils. Alternate methods to achieve this are required as the conventional methods are time-consuming an...
Principal Component Analysis Least-Squares Analysis Machine Learning Plant Leaves Phenotype Adaptation, Physiological Oryza Spectroscopy, Near-Infrared Salt Stress
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Convolutional decoding of thermographic images to locate and quantify honey adulterations.

Talanta Oct 24, 2019
In this research, 56 samples of pure honey have been mixed with different concentrations of rice syrup simulating a set of adulterated samples. A thermographic camera was used to extract data regarding the thermal development of the honey. The result...
Honey Neural Networks, Computer Thermography Time Factors Food Contamination Oryza
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Predicting rice blast disease: machine learning versus process-based models.

BMC bioinformatics Oct 22, 2019
BACKGROUND: In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and Water Accounting Rice Model (WARM)) and two approaches based on machine learning algorithms (M5Rules and Recurrent...
Neural Networks, Computer Computer Simulation Oryza Machine Learning Plant Diseases Algorithms
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QTG-Finder: A Machine-Learning Based Algorithm To Prioritize Causal Genes of Quantitative Trait Loci in Arabidopsis and Rice.

G3 (Bethesda, Md.) Oct 7, 2019
Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine ma...
Computational Biology Oryza Genes, Plant Quantitative Trait, Heritable Quantitative Trait Loci Algorithms Reproducibility of Results ROC Curve Machine Learning Software Web Browser Arabidopsis
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Distillation of crop models to learn plant physiology theories using machine learning.

PloS one May 29, 2019
Convolutional neural networks (CNNs) can not only classify images but can also generate key features, e.g., the Google neural network that learned to identify cats by simply watching YouTube videos, for the classification. In this paper, crop models ...
Oryza Algorithms Neural Networks, Computer Deep Learning Plant Physiological Phenomena Machine Learning Crops, Agricultural Models, Theoretical
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