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Viruses threatening stable production of cereal crops

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889196128 Year: Pages: 117 DOI: 10.3389/978-2-88919-612-8 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Microbiology
Added to DOAB on : 2016-08-16 10:34:25
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Cereal crops such as maize, wheat, and rice account for a majority of biomass produced globally in agriculture. Continuous economic and population growth especially in developing countries accompanied more intensive production of cereal crops to meet increasing demands for them as main staple foods and livestock feeds. However, imbalance between production and consumption of cereal crops, which is inevitably reflected as their higher market prices, is becoming palpable in recent years. Stable production of cereal crops has been threatened by various abiotic and biotic stresses. One of the most threatening constraints is virus diseases. Especially, intensification of cereal crop production is often achieved by monoculture of a popular crop variety in a wide area. Such agroecosystems with low biodiversity is usually more conducive to biotic stresses, and may result in the outbreaks of existing and emerging cereal viruses. Numerous reports on incidences of various virus diseases of cereal crops attested that viruses have been a long-standing obstacle eroding yields of cereal crops worldwide. Despite of the evident economic losses incurred by virus disease of cereal crops, the progress in basic research on virus species causing major diseases of cereal crops lagged behind compared to that carried out for viruses that can infect dicotyledonous plants. This was partially due to the lack of ideal experimental systems to investigate the interaction between viruses and monocotyledonous crops. For example, inoculation of many viruses to cereal plants still requires tedious manipulation of vector organisms, and reverse genetic systems are not available for many cereal viruses. However, application of latest molecular biology technologies has led to significant advance in cereal virology recently; transient gene expression systems through particle bombardment and agroinfiltration have been exploited to examine the functions of cereal virus proteins. Cell culture systems of vector insects enabled to investigate the molecular interactions between cereal viruses and insect vectors. Furthermore, RNAi technologies for vector insects and monocotyledonous plants facilitated identification of specific host and viral factors involved in viral replication and transmission cycles. Also, accumulating information on the genome sequences of cereal crop species has been simplifying the roadmap to pinpoint resistance genes against cereal viruses. The objective of this research topic is to provide and share the information which can contribute to advances in cereal virology by covering recent progresses in areas such as: 1) characterization of emerging viruses, 2) analyses of genetic and biological diversities within particular viruses, 3) development of experimental systems applicable to cereal viruses, 4) elucidation of the molecular interactions among viruses, vector organisms, and host plants, 5) identification of traits and genes linked to virus resistance in cereal crops, 6) development of novel genetic approaches for virus resistance, and 7) assessment of epidemiological factors affecting the incidences of cereal virus diseases. Synergistic integration of ideas from such areas under this research topic should help to formulate practical alternatives to the current management options for virus diseases in cereal crops.

Recent Advances in Flowering Time Control

Authors: --- --- --- --- et al.
Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889451159 Year: Pages: 255 DOI: 10.3389/978-2-88945-115-9 Language: English
Publisher: Frontiers Media SA
Subject: Genetics --- Botany --- Science (General)
Added to DOAB on : 2017-07-06 13:27:36
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The onset of flowering is an important step during the lifetime of a flowering plant. During the past two decades, there has been enormous progress in our understanding of how internal and external (environmental) cues control the transition to reproductive growth in plants. Many flowering time regulators have been identified from the model plant Arabidopsis thaliana. Most of them are assembled in regulatory pathways, which converge to central integrators which trigger the transition of the vegetative into an inflorescence meristem. For crop cultivation, the time of flowering is of upmost importance, because it determines yield. Phenotypic variation for this trait is largely controlled by genes, which were often modified during domestication or crop improvement. Understanding the genetic basis of flowering time regulation offers new opportunities for selection in plant breeding and for genome editing and genetic modification of crop species.

Sensors in Agriculture

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ISBN: 9783038974123 / 9783038974130 Year: Pages: 346 DOI: 10.3390/books978-3-03897-413-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:06
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Agriculture requires technical solutions for increasing production while lessening environmental impact by reducing the application of agro-chemicals and increasing the use of environmentally friendly management practices. A benefit of this is the reduction of production costs. Sensor technologies produce tools to achieve the abovementioned goals. The explosive technological advances and developments in recent years have enormously facilitated the attainment of these objectives, removing many barriers for their implementation, including the reservations expressed by farmers. Precision agriculture and ‘smart farming’ are emerging areas where sensor-based technologies play an important role. Farmers, researchers, and technical manufacturers are joining their efforts to find efficient solutions, improvements in production, and reductions in costs. This book brings together recent research and developments concerning novel sensors and their applications in agriculture. Sensors in agriculture are based on the requirements of farmers, according to the farming operations that need to be addressed.

Keywords

wireless sensor network (WSN) --- Wi-SUN --- vine --- mandarin orange --- thermal image --- fluorescent measurement --- X-ray fluorescence spectroscopy --- visible and near-infrared reflectance spectroscopy --- heavy metal contamination --- spectral pre-processing --- feature selection --- machine-learning --- LiDAR --- light-beam --- plant localization --- Kinect --- leaf area index --- radiative transfer model --- neural networks --- GF-1 satellite --- wide field view --- big data --- geo-information --- plant phenotyping --- grapevine breeding --- Vitis vinifera --- ambient intelligence --- wireless sensor --- fuzzy logic --- smart irrigation --- virtual organizations of agents --- CIE-Lab --- precision plant protection --- optical sensor --- weed control --- classification --- NIR hyperspectral imaging --- chemometrics analysis --- weeds --- UAS --- RPAS --- one-class --- machine learning --- remote sensing --- geoinformatics --- plant disease --- pest --- deep convolutional neural networks --- real-time processing --- detection --- hyperspectral imaging --- soil type classification --- total nitrogen --- texture features --- data fusion --- Fusarium --- near-infrared --- spectroscopy --- hulled barely --- partial least squares-discriminant analysis --- remote sensing --- precision agriculture --- crop monitoring --- data fusion --- speckle --- diffusion --- scattering --- biological sensing --- apparent soil electrical conductivity --- ECa-directed soil sampling --- electromagnetic induction --- proximal sensor --- response surface sampling --- salt tolerance --- boron tolerance --- soil mapping --- soil salinity --- spatial variability --- irrigation --- energy balance --- water management --- semi-arid regions --- on-line vis-NIR measurement --- total nitrogen --- total carbon --- spiking --- gradient boosted machines --- artificial neural networks --- random forests --- rice --- striped stem-borer --- hyperspectral imaging --- texture feature --- data fusion --- greenhouse --- wireless sensor network --- data fusion --- dynamic weight --- dataset --- agriculture --- obstacle detection --- computer vision --- cameras --- stereo imaging --- thermal imaging --- LiDAR --- radar --- object tracking --- crop area --- remote sensing image classification --- area frame sampling --- stratification --- regression estimator --- agriculture --- meat spoilage --- vegetable oil --- quality assessment --- electronic nose --- electrochemical sensors --- spectral analysis --- feature selection --- genetic algorithms --- classification --- vegetation indices --- vineyard --- diseases --- spatial data --- sensor --- data fusion --- change of support --- geostatistics --- precision agriculture --- management zones --- event detection --- back propagation model --- multivariate water quality parameters --- time-series data --- spatial-temporal model --- connected dominating set --- water supply network --- SS-OCT --- Capsicum annuum --- germination --- salt concentration --- deep learning --- clover-grass --- precision agriculture --- dry matter composition --- proximity sensing --- 3D reconstruction --- RGB-D sensor --- crop inspection platform --- water depth sensors --- soil moisture sensors --- temperature sensors --- rice field monitoring --- irrigation --- silage --- packing density --- moisture content --- compound sensor --- simultaneous measurement --- birth sensor --- bovine embedded hardware --- ambient intelligence --- virtual organizations of agents --- Fusarium --- near infrared --- discrimination --- hulled barely --- naked barley --- wheat --- dielectric probe --- apple shelf-life --- dielectric dispersion --- electronic nose --- pest scouting --- pest management --- gas sensor --- noninvasive detection --- nitrogen --- near infrared sensors --- drying temperature --- SPA-MLR --- PLS --- CARS --- hyperspectral camera --- handheld --- sensor evaluation --- case studies --- soil --- moisture --- sensor --- landslide --- rice leaves --- chromium content --- laser-induced breakdown spectroscopy --- laser wavelength --- preprocessing methods --- agricultural land --- field crops --- land cover --- photograph-grid method --- remote sensing --- data validation and calibration --- mobile app --- wireless sensor networks (WSN) --- energy efficiency --- distributed systems --- processing of sensed data --- WSN distribution algorithms --- recognition patterns --- agriculture

Sensors in Agriculture

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ISBN: 9783038977445 / 9783038977452 Year: Pages: 354 DOI: 10.3390/books978-3-03897-745-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:06
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Abstract

Agriculture requires technical solutions for increasing production while lessening environmental impact by reducing the application of agro-chemicals and increasing the use of environmentally friendly management practices. A benefit of this is the reduction of production costs. Sensor technologies produce tools to achieve the abovementioned goals. The explosive technological advances and developments in recent years have enormously facilitated the attainment of these objectives, removing many barriers for their implementation, including the reservations expressed by farmers. Precision agriculture and ‘smart farming’ are emerging areas where sensor-based technologies play an important role. Farmers, researchers, and technical manufacturers are joining their efforts to find efficient solutions, improvements in production, and reductions in costs. This book brings together recent research and developments concerning novel sensors and their applications in agriculture. Sensors in agriculture are based on the requirements of farmers, according to the farming operations that need to be addressed.

Keywords

wireless sensor network (WSN) --- Wi-SUN --- vine --- mandarin orange --- thermal image --- fluorescent measurement --- X-ray fluorescence spectroscopy --- visible and near-infrared reflectance spectroscopy --- heavy metal contamination --- spectral pre-processing --- feature selection --- machine-learning --- LiDAR --- light-beam --- plant localization --- Kinect --- leaf area index --- radiative transfer model --- neural networks --- GF-1 satellite --- wide field view --- big data --- geo-information --- plant phenotyping --- grapevine breeding --- Vitis vinifera --- ambient intelligence --- wireless sensor --- fuzzy logic --- smart irrigation --- virtual organizations of agents --- CIE-Lab --- precision plant protection --- optical sensor --- weed control --- classification --- NIR hyperspectral imaging --- chemometrics analysis --- weeds --- UAS --- RPAS --- one-class --- machine learning --- remote sensing --- geoinformatics --- plant disease --- pest --- deep convolutional neural networks --- real-time processing --- detection --- hyperspectral imaging --- soil type classification --- total nitrogen --- texture features --- data fusion --- Fusarium --- near-infrared --- spectroscopy --- hulled barely --- partial least squares-discriminant analysis --- remote sensing --- precision agriculture --- crop monitoring --- data fusion --- speckle --- diffusion --- scattering --- biological sensing --- apparent soil electrical conductivity --- ECa-directed soil sampling --- electromagnetic induction --- proximal sensor --- response surface sampling --- salt tolerance --- boron tolerance --- soil mapping --- soil salinity --- spatial variability --- irrigation --- energy balance --- water management --- semi-arid regions --- on-line vis-NIR measurement --- total nitrogen --- total carbon --- spiking --- gradient boosted machines --- artificial neural networks --- random forests --- rice --- striped stem-borer --- hyperspectral imaging --- texture feature --- data fusion --- greenhouse --- wireless sensor network --- data fusion --- dynamic weight --- dataset --- agriculture --- obstacle detection --- computer vision --- cameras --- stereo imaging --- thermal imaging --- LiDAR --- radar --- object tracking --- crop area --- remote sensing image classification --- area frame sampling --- stratification --- regression estimator --- agriculture --- meat spoilage --- vegetable oil --- quality assessment --- electronic nose --- electrochemical sensors --- spectral analysis --- feature selection --- genetic algorithms --- classification --- vegetation indices --- vineyard --- diseases --- spatial data --- sensor --- data fusion --- change of support --- geostatistics --- precision agriculture --- management zones --- event detection --- back propagation model --- multivariate water quality parameters --- time-series data --- spatial-temporal model --- connected dominating set --- water supply network --- SS-OCT --- Capsicum annuum --- germination --- salt concentration --- deep learning --- clover-grass --- precision agriculture --- dry matter composition --- proximity sensing --- 3D reconstruction --- RGB-D sensor --- crop inspection platform --- water depth sensors --- soil moisture sensors --- temperature sensors --- rice field monitoring --- irrigation --- silage --- packing density --- moisture content --- compound sensor --- simultaneous measurement --- birth sensor --- bovine embedded hardware --- ambient intelligence --- virtual organizations of agents --- Fusarium --- near infrared --- discrimination --- hulled barely --- naked barley --- wheat --- dielectric probe --- apple shelf-life --- dielectric dispersion --- electronic nose --- pest scouting --- pest management --- gas sensor --- noninvasive detection --- nitrogen --- near infrared sensors --- drying temperature --- SPA-MLR --- PLS --- CARS --- hyperspectral camera --- handheld --- sensor evaluation --- case studies --- soil --- moisture --- sensor --- landslide --- rice leaves --- chromium content --- laser-induced breakdown spectroscopy --- laser wavelength --- preprocessing methods --- agricultural land --- field crops --- land cover --- photograph-grid method --- remote sensing --- data validation and calibration --- mobile app --- wireless sensor networks (WSN) --- energy efficiency --- distributed systems --- processing of sensed data --- WSN distribution algorithms --- recognition patterns --- agriculture

Plant Proteomic Research 2.0

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ISBN: 9783039210626 / 9783039210633 Year: Pages: 594 DOI: 10.3390/books978-3-03921-063-3 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Biology --- Plant Sciences
Added to DOAB on : 2019-06-26 08:44:07
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Advancements in high-throughput “Omics” techniques have revolutionized plant molecular biology research. Proteomics offers one of the best options for the functional analysis of translated regions of the genome, generating a wealth of detailed information regarding the intrinsic mechanisms of plant stress responses. Various proteomic approaches are being exploited extensively for elucidating master regulator proteins which play key roles in stress perception and signaling, and these approaches largely involve gel-based and gel-free techniques, including both label-based and label-free protein quantification. Furthermore, post-translational modifications, subcellular localization, and protein–protein interactions provide deeper insight into protein molecular function. Their diverse applications contribute to the revelation of new insights into plant molecular responses to various biotic and abiotic stressors.

Keywords

Phalaenopsis --- petal --- pollination --- senescence --- 2-DE --- ROS --- Medicago sativa --- leaf cell wall proteome --- cadmium --- quantitative proteomics --- 2D DIGE --- chloroplast --- elevated CO2 --- heat stress --- nucleotide pyrophosphatase/phosphodiesterase --- (phospho)-proteomics --- photosynthesis --- protein phosphorylation --- 14-3-3 proteins --- Oryza sativa L. --- starch --- sucrose --- N utilization efficiency --- proteomics --- 2D --- protein phosphatase --- rice isogenic line --- SnRK1 --- 14-3-3 --- lettuce --- bolting --- proteome --- high temperature --- iTRAQ --- proteome profiling --- iTRAQ --- differentially abundant proteins (DAPs) --- drought stress --- physiological responses --- Zea mays L. --- GS3 --- ? subunit --- heterotrimeric G protein --- mass spectrometric analysis --- RGG3 --- rice --- western blotting --- Dn1-1 --- ?-subunit --- heterotrimeric G protein --- mass spectrometry analysis --- RGG4 --- rice --- western blotting --- Clematis terniflora DC. --- polyphenol oxidase --- virus induced gene silencing --- photosynthesis --- glycolysis --- Camellia sinensis --- chlorotic mutation --- chlorophyll deficiency --- weakening of carbon metabolism --- iTRAQ --- proteomics --- degradome --- wheat --- cultivar --- protease --- papain-like cysteine protease (PLCP) --- subtilase --- metacaspase --- caspase-like --- wheat leaf rust --- Puccinia recondita --- Stagonospora nodorum --- iTRAQ --- proteomics --- somatic embryogenesis --- pyruvate biosynthesis --- Zea mays --- chlorophylls --- LC-MS-based proteomics --- pea (Pisum sativum L.) --- proteome functional annotation --- proteome map --- seeds --- seed proteomics --- late blight disease --- potato proteomics --- Phytophthora infestans --- Sarpo Mira --- early and late disease stages --- Simmondsia chinensis --- cold stress --- proteomics --- leaf --- iTRAQ --- Ricinus communis L. --- cold stress --- seed imbibition --- iTRAQ --- proteomics --- Morus --- organ --- gel-free/label-free proteomics --- flavonoid --- antioxidant activity --- phosphoproteome --- barley --- seed dormancy --- germination --- imbibition --- after-ripening --- sugarcane --- Sporisorium scitamineum --- smut --- proteomics --- RT-qPCR --- ISR --- holm oak --- Quercus ilex --- 2-DE proteomics --- shotgun proteomics --- non-orthodox seed --- population variability --- stresses responses --- ammonium --- Arabidopsis thaliana --- carbon metabolism --- nitrogen metabolism --- nitrate --- proteomics --- root --- secondary metabolism --- proteomics --- wheat --- silver nanoparticles --- plant pathogenesis responses --- data-independent acquisition --- quantitative proteomics --- Pseudomonas syringae --- sweet potato plants infected by SPFMV --- SPV2 and SPVG --- sweet potato plants non-infected by SPFMV --- SPV2 and SPVG --- co-infection --- transcriptome profiling --- gene ontology --- pathway analysis --- lesion mimic mutant --- leaf spot --- phenylpropanoid biosynthesis --- proteomics --- isobaric tags for relative and absolute quantitation (iTRAQ) --- rice --- affinity chromatography --- ergosterol --- fungal perception --- innate immunity --- pattern recognition receptors --- plasma membrane --- proteomics --- proteomics --- maize --- plant-derived smoke --- shoot --- Solanum tuberosum --- patatin --- seed storage proteins --- vegetative storage proteins --- tuber phosphoproteome --- targeted two-dimensional electrophoresis --- B. acuminata petals --- MALDI-TOF/TOF --- GC-TOF-MS --- qRT-PCR --- differential proteins --- n/a

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