Important wheat diseases in the US and their management in the 21st century

Frontiers in Plant Science, Journal Year: 2023, Volume and Issue: 13

Published: Jan. 12, 2023

Wheat is a crop of historical significance, as it marks the turning point human civilization 10,000 years ago with its domestication. Due to rapid increase in population, wheat production needs be increased by 50% 2050 and this growth will mainly based on yield increases, there strong competition for scarce productive arable land from other sectors. This increasing demand can further achieved using sustainable approaches including integrated disease pest management, adaption warmer climates, less use water resources frequency abiotic stress tolerances. Out 200 diseases wheat, 50 cause economic losses are widely distributed. Each year, about 20% lost due diseases. Some major rusts, smut, tan spot, spot blotch, fusarium head blight, common root rot, septoria powdery mildew, blast, several viral, nematode, bacterial These badly impact mortality plants. review focuses important present United States, comprehensive information causal organism, damage, symptoms host range, favorable conditions, management strategies. Furthermore, genetic breeding efforts control manage these discussed. A detailed description all QTLs, genes reported cloned provided review. study utmost importance programs throughout world breed resistance under changing environmental conditions.

Language: Английский

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Climate Change—The Rise of Climate-Resilient Crops

Plants, Journal Year: 2024, Volume and Issue: 13(4), P. 490 - 490

Published: Feb. 8, 2024

Climate change disrupts food production in many regions of the world. The accompanying extreme weather events, such as droughts, floods, heat waves, and cold snaps, pose threats to crops. concentration carbon dioxide also increases atmosphere. United Nations is implementing climate-smart agriculture initiative ensure security. An element this project involves breeding climate-resilient crops or plant cultivars with enhanced resistance unfavorable environmental conditions. Modern agriculture, which currently homogeneous, needs diversify species cultivated plants. Plant programs should extensively incorporate new molecular technologies, supported by development field phenotyping techniques. Breeders closely cooperate scientists from various fields science.

Language: Английский

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Optimizing Crop Production With Plant Phenomics Through High‐Throughput Phenotyping and AI in Controlled Environments

Food and Energy Security, Journal Year: 2025, Volume and Issue: 14(1)

Published: Jan. 1, 2025

ABSTRACT Plant phenomics deals with the measurement of plant phenotypes associated genetic and environmental variation in controlled environment agriculture (CEA). Encompassing a spectrum from molecular biology to ecosystem‐level studies, it employs high‐throughput phenotyping (HTP) approaches quickly evaluate characteristics enhance yields crops smart facilities. HTP uses parameters for accuracy, such as software sensors, well hyperspectral imaging pigment data, thermal water content, fluorescence photosynthesis rates. They provide information on growth kinetics, physiological biochemical characteristics, genotype–environment interaction. Artificial intelligence (AI) machine learning (ML) are used large volume phenotypic data predict rates, determine optimal time plants, or detect diseases, nutrient deficiencies, pests at an early stage. The lighting factories is adjusted based specific phase using different light intensities, spectrums, durations germination, vegetative growth, flowering stages, hydroponics method providing nutrients, CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) improving certain resistance drought. These systems crop production, yields, adaptability, input use by optimizing utilizing precision breeding techniques. AI combination several disciplines, promoting understanding plant–environment interactions relation problems resource use, climate change. It affects their capacity develop that capture inputs, minimize chemical application, resilient Phenomics cost‐effective, reduces contributes more sustainable agricultural practices, being economically environmentally sound. Altogether, central CEA due its capitalize potential within advance sustainability food security. Through phenomic research, next advancements likely be even revolutionary terms practices worldwide.

Language: Английский

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Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Aug. 11, 2022

In wheat, a meta-analysis was performed using previously identified QTLs associated with drought stress (DS), heat (HS), salinity (SS), water-logging (WS), pre-harvest sprouting (PHS), and aluminium (AS) which predicted total of 134 meta-QTLs (MQTLs) that involved at least 28 consistent stable MQTLs conferring tolerance to five or all six abiotic stresses under study. Seventy-six out the 132 physically anchored were also verified genome-wide association studies. Around 43% had genetic physical confidence intervals less than 1 cM 5 Mb, respectively. Consequently, 539 genes in some selected providing 6 stresses. Comparative analysis underlying four RNA-seq based transcriptomic datasets unravelled 189 differentially expressed included 11 most promising candidate common among different datasets. The promoter showed promoters these include many responsiveness cis-regulatory elements, such as ARE, MBS, TC-rich repeats, As-1 element, STRE, LTR, WRE3, WUN-motif others. Further, overlapped 34 known genes. addition, numerous ortho-MQTLs maize, rice genomes discovered. These findings could help fine mapping gene cloning, well marker-assisted breeding for multiple tolerances wheat.

Language: Английский

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Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Journal of Integrative Agriculture, Journal Year: 2023, Volume and Issue: 23(6), P. 1787 - 1802

Published: Oct. 18, 2023

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture. Recent advancements in high-throughput phenotyping technologies artificial intelligence (AI) have revolutionized field, enabling rapid accurate assessment crop traits on a large scale. The integration AI machine learning algorithms with data has unlocked new opportunities improvement. can analyze interpret datasets, extracting meaningful patterns correlations between phenotypic genetic factors. These potential to revolutionize plant breeding programs by providing breeders efficient tools trait selection, reducing time cost required variety development. However, further research collaborations are needed overcome fully unlock power By leveraging algorithms, researchers efficiently data, uncover complex patterns, establish predictive models that enable precise selection breeding. aim this review explore transformative integrating will encompass an in-depth analysis recent applications, highlighting numerous benefits associated intelligence.

Language: Английский

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An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

Biology, Journal Year: 2023, Volume and Issue: 12(10), P. 1298 - 1298

Published: Sept. 30, 2023

This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods phenotyping, while valuable, are limited their ability to capture complexity biology. advent (meta-)genomics, (meta-)transcriptomics, proteomics, metabolomics has provided an opportunity for a more comprehensive analysis. AI machine learning (ML) techniques can effectively handle volume data, providing meaningful interpretations predictions. Reflecting multidisciplinary nature this area review, readers will find collection state-of-the-art solutions that key integration phenotyping experiments horticulture, including experimental design considerations with several technical non-technical challenges, which discussed along solutions. future prospects include precision predictive breeding, improved disease stress response management, sustainable crop exploration biodiversity. holds immense promise revolutionizing research applications, heralding new era

Language: Английский

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Redesigning crop varieties to win the race between climate change and food security

Molecular Plant, Journal Year: 2023, Volume and Issue: 16(10), P. 1590 - 1611

Published: Sept. 7, 2023

Climate change poses daunting challenges to agricultural production and food security. Rising temperatures, shifting weather patterns, more frequent extreme events have already demonstrated their effects on local, regional, global systems. Crop varieties that withstand climate-related stresses are suitable for cultivation in innovative cropping systems will be crucial maximize risk avoidance, productivity, profitability under climate-changed environments. We surveyed 588 expert stakeholders predict current novel traits may essential future pearl millet, sorghum, maize, groundnut, cowpea, common bean varieties, particularly sub-Saharan Africa. then review the progress prospects breeding three prioritized future-essential each of these crops. Experts most priorities remain important, but rates genetic gain must increase keep pace with climate consumer demands. Importantly, predicted include targets also prioritized; example, (1) optimized rhizosphere microbiome, benefits P, N, water use efficiency, (2) performance across or specific systems, (3) lower nighttime respiration, (4) improved stover quality, (5) increased early vigor. further discuss cutting-edge tools approaches discover, validate, incorporate diversity from exotic germplasm into populations unprecedented precision, accuracy, speed. conclude greatest challenge developing crop win race between security might our innovativeness defining boldness breed tomorrow.

Language: Английский

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Cyber-agricultural systems for crop breeding and sustainable production

Trends in Plant Science, Journal Year: 2023, Volume and Issue: 29(2), P. 130 - 149

Published: Aug. 28, 2023

The cyber-agricultural system (CAS) represents an overarching framework of agriculture that leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and scalable cyberinfrastructure (CI) both breeding production agriculture. We discuss the progress perspective three fundamental components CAS - modeling, actuation emerging concept agricultural digital twins (DTs). also how CI is becoming a key enabler In this review we shed light on significance revolutionizing crop by enhancing efficiency, productivity, sustainability, resilience to changing climate. Finally, identify underexplored promising future directions for research development.

Language: Английский

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Automatic acquisition, analysis and wilting measurement of cotton 3D phenotype based on point cloud

Biosystems Engineering, Journal Year: 2024, Volume and Issue: 239, P. 173 - 189

Published: Feb. 28, 2024

Language: Английский

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Genomics, Phenomics, and Machine Learning in Transforming Plant Research: Advancements and Challenges

Horticultural Plant Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 1, 2024

Advances in gene editing and natural genetic variability present significant opportunities to generate novel alleles select sources of variation for horticulture crop improvement. The improvement crops enhance their resilience abiotic stresses new pests due climate change is essential future food security. field genomics has made strides over the past few decades, enabling us sequence analyze entire genomes. However, understanding complex relationship between genes expression phenotypes - observable characteristics an organism requires a deeper phenomics. Phenomics seeks link information with biological processes environmental factors better understand traits diseases. Recent breakthroughs this include development advanced imaging technologies, artificial intelligence algorithms, large-scale data analysis techniques. These tools have enabled explore relationships genotype, phenotype, environment unprecedented detail. This review explores importance phenotypes. Integration efficient high throughput plant phenotyping as well potential machine learning approaches genomic phenomics trait discovery.

Language: Английский

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