Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book is the first volume of a comprehensive assemblage of contemporary knowledge relevant to genomics and other omics in date palm. Volume 1 consists of 11 chapters arranged in 3 parts grouped according to subject. Part I, Biology and Phylogeny, focuses on date palm biology, evolution and origin. Part II, Biodiversity and Molecular Identification, covers conformity of in vitro derived plants, molecular markers, barcoding, pollinizer genetics and gender determination. Part III, Genome Mapping and Bioinformatics, addresses genome mapping of nuclear, chloroplast and mitochondrial DNA, in addition to a chapter on progress made in date palm bioinformatics. This volume represents the efforts of 30 international scientists from 10 countries and contains 78 figures and 30 tables to illustrate presented concepts. Volume 2 is published under the title: Omics and Molecular Breeding.

Date palm --- Genetics. --- Phoenix dactylifera --- Phoenix palms --- Palmera de dàtils --- Genomes --- Genoma --- Genoma humà --- Genòmica --- Dàtils --- Plant biotechnology. --- Plant genetics. --- Agriculture. --- Plant Biotechnology. --- Plant Genetics. --- Farming --- Husbandry --- Industrial arts --- Life sciences --- Food supply --- Land use, Rural --- Plants --- Genetics --- Crop biotechnology --- Crops --- Agricultural biotechnology --- Biotechnology

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Woody biomass is most widely used for energy production. In the United States, roughly 2% of the energy consumed annually is generated from wood and wood-derived fuels. Woody biomass needs to be preprocessed and pretreated before it is used for energy production. Preprocessing and pretreatments improve the physical, chemical, and rheological properties, making them more suitable for feeding, handling, storage transportation, and conversion. Mechanical preprocessing technologies such as size reduction and densification, help improve particle size distribution and density. Thermal pretreatment can reduce grinding energy and torrefied ground biomass has improved sphericity, particle surface area, and particle size distribution. This book focuses on several specific topics, such as understanding how forest biomass for biofuels impacts greenhouse gas emissions; mechanical preprocessing, such as densification of forest residue biomass, to improve physical properties such as size, shape, and density; the impact of thermal pretreatment temperatures on woody biomass chemical composition, physical properties, and microstructure for thermochemical conversions such as pyrolysis and gasification; the grindability of torrefied pellets; use of wood for gasification and as a filter for tar removal; and understanding the pyrolysis kinetics of biomass using thermogravimetric analyzers.

grindability --- torrefied biomass --- pellet --- energy consumption --- co-firing --- biomass --- gasification --- tar --- syngas cleaning --- dry filter --- pyrolysis --- chemical composition --- micro-structure --- physical properties --- scanning electron microscopy --- wood --- thermal pretreatment --- torrefaction --- timber --- harvest residues --- ethanol --- GHG savings --- Michigan --- variety and rootstock selection --- almond tree --- agricultural practices --- halophytes --- Phoenix dactylifera --- Salicornia bigelovii --- thermogravimetric analysis --- torrefied biomass --- correlation --- ultimate analysis --- solid yield --- heating value --- OLS --- 2-inch top pine residue + switchgrass blends --- pelleting process variables --- pellet quality --- specific energy consumption --- response surface models --- hybrid genetic algorithm --- pelleting --- functional groups --- pellet strength --- combustion efficiency --- forest biomass --- Australia --- biomass energy potential --- emission --- bioenergy

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- artificial neural network (ANN) --- Grain weevil identification --- neural modelling classification --- winter wheat --- grain --- artificial neural network --- ferulic acid --- deoxynivalenol --- nivalenol --- MLP network --- sensitivity analysis --- precision agriculture --- machine learning --- similarity --- metric --- memory --- deep learning --- plant growth --- dynamic response --- root zone temperature --- dynamic model --- NARX neural networks --- hydroponics --- vegetation indices --- UAV --- neural network --- corn plant density --- corn canopy cover --- yield prediction --- CLQ --- GA-BPNN --- GPP-driven spectral model --- rice phenology --- EBK --- correlation filter --- crop yield prediction --- hybrid feature extraction --- recursive feature elimination wrapper --- artificial neural networks --- big data --- classification --- high-throughput phenotyping --- modeling --- predicting --- time series forecasting --- soybean --- food production --- paddy rice mapping --- dynamic time warping --- LSTM --- weakly supervised learning --- cropland mapping --- apparent soil electrical conductivity (ECa) --- magnetic susceptibility (MS) --- EM38 --- neural networks --- Phoenix dactylifera L. --- Medjool dates --- image classification --- convolutional neural networks --- transfer learning --- average degree of coverage --- coverage unevenness coefficient --- optimization --- high-resolution imagery --- oil palm tree --- CNN --- Faster-RCNN --- image identification --- agroecology --- weeds --- yield gap --- environment --- health --- crop models --- soil and plant nutrition --- automated harvesting --- model application for sustainable agriculture --- remote sensing for agriculture --- decision supporting systems --- neural image analysis

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- artificial neural network (ANN) --- Grain weevil identification --- neural modelling classification --- winter wheat --- grain --- artificial neural network --- ferulic acid --- deoxynivalenol --- nivalenol --- MLP network --- sensitivity analysis --- precision agriculture --- machine learning --- similarity --- metric --- memory --- deep learning --- plant growth --- dynamic response --- root zone temperature --- dynamic model --- NARX neural networks --- hydroponics --- vegetation indices --- UAV --- neural network --- corn plant density --- corn canopy cover --- yield prediction --- CLQ --- GA-BPNN --- GPP-driven spectral model --- rice phenology --- EBK --- correlation filter --- crop yield prediction --- hybrid feature extraction --- recursive feature elimination wrapper --- artificial neural networks --- big data --- classification --- high-throughput phenotyping --- modeling --- predicting --- time series forecasting --- soybean --- food production --- paddy rice mapping --- dynamic time warping --- LSTM --- weakly supervised learning --- cropland mapping --- apparent soil electrical conductivity (ECa) --- magnetic susceptibility (MS) --- EM38 --- neural networks --- Phoenix dactylifera L. --- Medjool dates --- image classification --- convolutional neural networks --- transfer learning --- average degree of coverage --- coverage unevenness coefficient --- optimization --- high-resolution imagery --- oil palm tree --- CNN --- Faster-RCNN --- image identification --- agroecology --- weeds --- yield gap --- environment --- health --- crop models --- soil and plant nutrition --- automated harvesting --- model application for sustainable agriculture --- remote sensing for agriculture --- decision supporting systems --- neural image analysis