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Open access, peer reviewed, online journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data.

Bioinformatics --- Computational biology --- Data mining --- Bio-informatique --- Exploration de données (Informatique) --- Periodicals. --- Périodiques --- Computational Biology. --- Bioinformatics. --- Computational biology. --- Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Bio-informatics --- Biological informatics --- Bio-Informatics --- Biology, Computational --- Computational Molecular Biology --- Molecular Biology, Computational --- Bio Informatics --- Bio-Informatic --- Bioinformatic --- Biologies, Computational Molecular --- Biology, Computational Molecular --- Computational Molecular Biologies --- Molecular Biologies, Computational --- data mining --- machine learning algorithms --- large scale data analysis --- Database searching --- Biology --- Information science --- Systems biology --- Genomics --- Data processing --- Data Mining. --- Biomedicine. --- Mathematical analysis --- Human medicine --- Computer. Automation --- Computational Chemistry --- Biology - General

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This Special Issue on ‘Advances in Cereal Crops Breeding’ comprises 10 papers covering a wide range of subjects, including the expression-level investigation of genes in terms of salinity stress adaptations and their relationships with proteomics in rice, the use of genetic analysis to assess the general combining ability (GCA) and specific combining ability (SCA) in promising hybrids of maize, the use of DNA markers based on PCR in rice, the identification of quantitative trait loci (QTLs) in wheat and simple sequence repeats (SSR) in rice, the use of single-nucleotide polymorphisms (SNP) in a genome-wide association study (GWAS) in cereals, and Nanopore direct RNA sequencing of related with LTR RNA retrotransposon in triticale prior to the genomic selection of heterotic maize hybrids.

maize --- density tolerance --- combining ability --- gene effects --- genetic diversity --- rice --- salinity --- submergence tolerance --- blast --- SSR markers --- PCR analysis --- long non-coding RNAs --- seed development --- Nanopore sequencing --- retrotransposons --- triticale --- prediction accuracy --- mixed linear and Bayesian models --- machine learning algorithms --- training set size and composition --- parametric and nonparametric models --- drought stress --- dendrogram --- barley --- breeding --- marker-assisted selection --- genes --- genetic resources --- genome editing --- health benefits --- metabolomics --- oat --- QTL --- wheat --- Triticum aestivum L. --- QMrl-7B --- root traits --- grain yield --- nitrogen use efficiency --- GWAS --- salinity tolerance --- Vietnamese landraces --- abiotic stress --- root --- auxin --- YUCCA --- PIN --- proteomics --- mass spectrometry --- n/a

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This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques.

Research & information: general --- Geography --- AGB estimation and mapping --- mangroves --- UAV LiDAR --- WorldView-2 --- terrestrial laser scanning --- above-ground biomass --- nondestructive method --- DBH --- bark roughness --- Landsat dataset --- forest AGC estimation --- random forest --- spatiotemporal evolution --- aboveground biomass --- variable selection --- forest type --- machine learning --- subtropical forests --- Landsat 8 OLI --- seasonal images --- stepwise regression --- map quality --- subtropical forest --- urban vegetation --- biomass estimation --- Sentinel-2A --- Xuzhou --- forest biomass estimation --- forest inventory data --- multisource remote sensing --- biomass density --- ecosystem services --- trade-off --- synergy --- multiple ES interactions --- valley basin --- norway spruce --- LiDAR --- allometric equation --- individual tree detection --- tree height --- diameter at breast height --- GEOMON --- ALOS-2 L band SAR --- Sentinel-1 C band SAR --- Sentinel-2 MSI --- ALOS DSM --- stand volume --- support vector machine for regression --- ordinary kriging --- forest succession --- leaf area index --- plant area index --- machine learning algorithms --- forest growing stock volume --- SPOT6 imagery --- Pinus massoniana plantations --- sentinel 2 --- landsat --- remote sensing --- GIS --- shrubs biomass --- bioenergy --- vegetation indices

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This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques.

Research & information: general --- Geography --- AGB estimation and mapping --- mangroves --- UAV LiDAR --- WorldView-2 --- terrestrial laser scanning --- above-ground biomass --- nondestructive method --- DBH --- bark roughness --- Landsat dataset --- forest AGC estimation --- random forest --- spatiotemporal evolution --- aboveground biomass --- variable selection --- forest type --- machine learning --- subtropical forests --- Landsat 8 OLI --- seasonal images --- stepwise regression --- map quality --- subtropical forest --- urban vegetation --- biomass estimation --- Sentinel-2A --- Xuzhou --- forest biomass estimation --- forest inventory data --- multisource remote sensing --- biomass density --- ecosystem services --- trade-off --- synergy --- multiple ES interactions --- valley basin --- norway spruce --- LiDAR --- allometric equation --- individual tree detection --- tree height --- diameter at breast height --- GEOMON --- ALOS-2 L band SAR --- Sentinel-1 C band SAR --- Sentinel-2 MSI --- ALOS DSM --- stand volume --- support vector machine for regression --- ordinary kriging --- forest succession --- leaf area index --- plant area index --- machine learning algorithms --- forest growing stock volume --- SPOT6 imagery --- Pinus massoniana plantations --- sentinel 2 --- landsat --- remote sensing --- GIS --- shrubs biomass --- bioenergy --- vegetation indices --- AGB estimation and mapping --- mangroves --- UAV LiDAR --- WorldView-2 --- terrestrial laser scanning --- above-ground biomass --- nondestructive method --- DBH --- bark roughness --- Landsat dataset --- forest AGC estimation --- random forest --- spatiotemporal evolution --- aboveground biomass --- variable selection --- forest type --- machine learning --- subtropical forests --- Landsat 8 OLI --- seasonal images --- stepwise regression --- map quality --- subtropical forest --- urban vegetation --- biomass estimation --- Sentinel-2A --- Xuzhou --- forest biomass estimation --- forest inventory data --- multisource remote sensing --- biomass density --- ecosystem services --- trade-off --- synergy --- multiple ES interactions --- valley basin --- norway spruce --- LiDAR --- allometric equation --- individual tree detection --- tree height --- diameter at breast height --- GEOMON --- ALOS-2 L band SAR --- Sentinel-1 C band SAR --- Sentinel-2 MSI --- ALOS DSM --- stand volume --- support vector machine for regression --- ordinary kriging --- forest succession --- leaf area index --- plant area index --- machine learning algorithms --- forest growing stock volume --- SPOT6 imagery --- Pinus massoniana plantations --- sentinel 2 --- landsat --- remote sensing --- GIS --- shrubs biomass --- bioenergy --- vegetation indices

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Remote image capture systems are a key element in efficient and sustainable agriculture nowadays. They are increasingly being used to obtain information of interest from the crops, the soil and the environment. It includes different types of capturing devices: from satellites and drones, to in-field devices; different types of spectral information, from visible RGB images, to multispectral images; different types of applications; and different types of techniques in the areas of image processing, computer vision, pattern recognition and machine learning. This book covers all these aspects, through a series of chapters that describe specific recent applications of these techniques in interesting problems of agricultural engineering.

History of engineering & technology --- SVM --- budding rate --- UAV --- geometric consistency --- radiometric consistency --- point clouds --- ICP --- reflectance maps --- vegetation indices --- Parrot Sequoia --- artificial intelligence --- precision agriculture --- agricultural robot --- optimization algorithm --- online operation --- segmentation --- coffee leaf rust --- machine learning --- deep learning --- remote sensing --- Fourth Industrial Revolution --- Agriculture 4.0 --- failure strain --- sandstone --- digital image correlation --- Hill-Tsai failure criterion --- finite element method --- reference evapotranspiration --- moisture sensors --- machine learning regression --- frequency-domain reflectometry --- randomizable filtered classifier --- convolutional neural network --- U-Net --- land use --- banana plantation --- Panama TR4 --- aerial photography --- remote images --- systematic mapping study --- agriculture --- applications --- total leaf area --- mixed pixels --- Cabernet Sauvignon --- NDVI --- Normalized Difference Vegetation Index --- precision viticulture --- 3D model --- spatial vision --- fertirrigation --- teaching-learning --- spectrometry --- Sentinel-2 --- pasture quality index --- normalized difference vegetation index --- normalized difference water index --- supplementation --- decision making --- digital agriculture --- grape yield estimate --- berries counting --- Dilated CNN --- machine learning algorithms --- classification performance --- winter wheat mapping --- large-scale --- water stress --- Prunus avium L. --- stem water potential --- low-cost thermography --- thermal indexes --- canopy temperature --- non-water-stressed baselines --- non-transpiration baseline --- soil moisture --- andosols --- image processing --- greenhouse --- automatic tomato harvesting --- SVM --- budding rate --- UAV --- geometric consistency --- radiometric consistency --- point clouds --- ICP --- reflectance maps --- vegetation indices --- Parrot Sequoia --- artificial intelligence --- precision agriculture --- agricultural robot --- optimization algorithm --- online operation --- segmentation --- coffee leaf rust --- machine learning --- deep learning --- remote sensing --- Fourth Industrial Revolution --- Agriculture 4.0 --- failure strain --- sandstone --- digital image correlation --- Hill-Tsai failure criterion --- finite element method --- reference evapotranspiration --- moisture sensors --- machine learning regression --- frequency-domain reflectometry --- randomizable filtered classifier --- convolutional neural network --- U-Net --- land use --- banana plantation --- Panama TR4 --- aerial photography --- remote images --- systematic mapping study --- agriculture --- applications --- total leaf area --- mixed pixels --- Cabernet Sauvignon --- NDVI --- Normalized Difference Vegetation Index --- precision viticulture --- 3D model --- spatial vision --- fertirrigation --- teaching-learning --- spectrometry --- Sentinel-2 --- pasture quality index --- normalized difference vegetation index --- normalized difference water index --- supplementation --- decision making --- digital agriculture --- grape yield estimate --- berries counting --- Dilated CNN --- machine learning algorithms --- classification performance --- winter wheat mapping --- large-scale --- water stress --- Prunus avium L. --- stem water potential --- low-cost thermography --- thermal indexes --- canopy temperature --- non-water-stressed baselines --- non-transpiration baseline --- soil moisture --- andosols --- image processing --- greenhouse --- automatic tomato harvesting