Applications Of Artificial Intelligence Machine Learning And Deep Learning In Plant Breeding
DOWNLOAD
Download Applications Of Artificial Intelligence Machine Learning And Deep Learning In Plant Breeding PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Applications Of Artificial Intelligence Machine Learning And Deep Learning In Plant Breeding book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Applications Of Artificial Intelligence Machine Learning And Deep Learning In Plant Breeding
DOWNLOAD
Author : Maliheh Eftekhari
language : en
Publisher: Frontiers Media SA
Release Date : 2024-05-29
Applications Of Artificial Intelligence Machine Learning And Deep Learning In Plant Breeding written by Maliheh Eftekhari and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-29 with Science categories.
Artificial Intelligence (AI) is an extensive concept that can be interpreted as a concentration on designing computer programs to train machines to accomplish functions like or better than hu-mans. An important subset of AI is Machine Learning (ML), in which a computer is provided with the capacity to learn its own patterns instead of the patterns and restrictions set by a human programmer, thus improving from experience. Deep Learning (DL), as a class of ML techniques, employs multilayered neural networks. The application of AI to plant science research is new and has grown significantly in recent years due to developments in calculation power, proficien-cies of hardware, and software progress. AI algorithms try to provide classifications and predic-tions. As applied to plant breeding, particularly omics data, ML as a given AI algorithm tries to translate omics data, which are intricate and include nonlinear interactions, into precise plant breeding. The applications of AI are extending rapidly and enhancing intensely in sophistication owing to the capability of rapid processing of huge and heterogeneous data. The conversion of AI techniques into accurate plant breeding is of great importance and will play a key role in the new era of plant breeding techniques in the coming years, particularly multi-omics data analysis. Advancements in plant breeding mainly depend upon developing statistical methods that harness the complicated data provided by analytical technologies identifying and quantifying genes, transcripts, proteins, metabolites, etc. The systems biology approach used in plant breeding, which integrates genomics, transcriptomics, proteomics, metabolomics, and other omics data, provides a massive amount of information. It is essential to perform accurate statistical analyses and AI methods such as ML and DL as well as optimization techniques to not only achieve an understanding of networks regulation and plant cell functions but develop high-precision models to predict the reaction of new Genetically Modified (GM) plants in special conditions. The constructed models will be of great economic importance, significantly reducing the time, labor, and instrument costs when finding optimized conditions for the bio-exploitation of plants. This Research Topic covers a wide range of studies on artificial intelligence-assisted plant breeding techniques, which contribute to plant biology and plant omics research. The relevant sub-topics include, but are not restricted to, the following: • AI-assisted plant breeding using omics and multi-omics approaches • Applying AI techniques along with multi-omics to recognize novel biomarkers associated with plant biological activities • Constructing up-to-date ML modeling and analyzing methods for dealing with omics data related to different plant growth processes • AI-assisted omics techniques in the plant defense process • Combining AI-assisted omics and multi-omics techniques using plant system biology approaches • Combining bioinformatics tools with AI approaches to analyze plant omics data • Designing cutting-edge workflow and developing innovative AI biology methods for omics data analysis
Plantomics The Omics Of Plant Science
DOWNLOAD
Author : Debmalya Barh
language : en
Publisher: Springer
Release Date : 2015-03-18
Plantomics The Omics Of Plant Science written by Debmalya Barh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-18 with Science categories.
PlantOmics: The Omics of Plant Science provides a comprehensive account of the latest trends and developments of omics technologies or approaches and their applications in plant science. Thirty chapters written by 90 experts from 15 countries are included in this state-of-the-art book. Each chapter describes one topic/omics such as: omics in model plants, spectroscopy for plants, next generation sequencing, functional genomics, cyto-metagenomics, epigenomics, miRNAomics, proteomics, metabolomics, glycomics, lipidomics, secretomics, phenomics, cytomics, physiomics, signalomics, thiolomics, organelle omics, micro morphomics, microbiomics, cryobionomics, nanotechnology, pharmacogenomics, and computational systems biology for plants. It provides up to date information, technologies, and their applications that can be adopted and applied easily for deeper understanding plant biology and therefore will be helpful in developing the strategy for generating cost-effective superior plants for various purposes. In the last chapter, the editors have proposed several new areas in plant omics that may be explored in order to develop an integrated meta-omics strategy to ensure the world and earth’s health and related issues. This book will be a valuable resource to students and researchers in the field of cutting-edge plant omics.
Elements Of Causal Inference
DOWNLOAD
Author : Jonas Peters
language : en
Publisher: MIT Press
Release Date : 2017-11-29
Elements Of Causal Inference written by Jonas Peters and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-29 with Computers categories.
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
Agriculture 5 0
DOWNLOAD
Author : Latief Ahmad
language : en
Publisher: CRC Press
Release Date : 2021-03-24
Agriculture 5 0 written by Latief Ahmad and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-24 with Science categories.
Agriculture 5.0: Artificial Intelligence, IoT & Machine Learning provides an interdisciplinary, integrative overview of latest development in the domain of smart farming. It shows how the traditional farming practices are being enhanced and modified by automation and introduction of modern scalable technological solutions that cut down on risks, enhance sustainability, and deliver predictive decisions to the grower, in order to make agriculture more productive. An elaborative approach has been used to highlight the applicability and adoption of key technologies and techniques such WSN, IoT, AI and ML in agronomic activities ranging from collection of information, analysing and drawing meaningful insights from the information which is more accurate, timely and reliable.It synthesizes interdisciplinary theory, concepts, definitions, models and findings involved in complex global sustainability problem-solving, making it an essential guide and reference. It includes real-world examples and applications making the book accessible to a broader interdisciplinary readership. This book clarifies hoe the birth of smart and intelligent agriculture is being nurtured and driven by the deployment of tiny sensors or AI/ML enabled UAV’s or low powered Internet of Things setups for the sensing, monitoring, collection, processing and storing of the information over the cloud platforms. This book is ideal for researchers, academics, post-graduate students and practitioners of agricultural universities, who want to embrace new agricultural technologies for Determination of site-specific crop requirements, future farming strategies related to controlling of chemical sprays, yield, price assessments with the help of AI/ML driven intelligent decision support systems and use of agri-robots for sowing and harvesting. The book will be covering and exploring the applications and some case studies of each technology, that have heavily made impact as grand successes. The main aim of the book is to give the readers immense insights into the impact and scope of WSN, IoT, AI and ML in the growth of intelligent digital farming and Agriculture revolution 5.0.The book also focuses on feasibility of precision farming and the problems faced during adoption of precision farming techniques, its potential in India and various policy measures taken all over the world. The reader can find a description of different decision support tools like crop simulation models, their types, and application in PA. Features: Detailed description of the latest tools and technologies available for the Agriculture 5.0. Elaborative information for different type of hardware, platforms and machine learning techniques for use in smart farming. Elucidates various types of predictive modeling techniques available for intelligent and accurate agricultural decision making from real time collected information for site specific precision farming. Information about different type of regulations and policies made by all over the world for the motivation farmers and innovators to invest and adopt the AI and ML enabled tools and farming systems for sustainable production.
Artificial Neural Networks In Agriculture
DOWNLOAD
Author : Sebastian Kujawa
language : en
Publisher: Mdpi AG
Release Date : 2021-11-11
Artificial Neural Networks In Agriculture written by Sebastian Kujawa and has been published by Mdpi AG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-11 with Technology & Engineering categories.
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.
Plant Breeding Technology
DOWNLOAD
Author : Zeba Khan
language : en
Publisher: CABI
Release Date : 2024-12-26
Plant Breeding Technology written by Zeba Khan and has been published by CABI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-26 with Technology & Engineering categories.
This book highlights the latest and most exciting technological tools for plant breeding that are becoming the staple of all crop improvement programs. Using case studies and an evidence-based approach to examine the current status of each technique, the book discusses the challenges so far uncovered and future trends. The book presents a systematic guide to various genomics approaches deployed for trait discovery and improvement in crop species. The chapters comprehensively cover each application, its advantages, and disadvantages, and its potential for improvement, supported by illustrative examples.
Computer Vision And Machine Learning In Agriculture
DOWNLOAD
Author : Mohammad Shorif Uddin
language : en
Publisher: Springer Nature
Release Date : 2021-03-23
Computer Vision And Machine Learning In Agriculture written by Mohammad Shorif Uddin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-23 with Technology & Engineering categories.
This book discusses computer vision, a noncontact as well as a nondestructive technique involving the development of theoretical and algorithmic tools for automatic visual understanding and recognition which finds huge applications in agricultural productions. It also entails how rendering of machine learning techniques to computer vision algorithms is boosting this sector with better productivity by developing more precise systems. Computer vision and machine learning (CV-ML) helps in plant disease assessment along with crop condition monitoring to control the degradation of yield, quality, and severe financial loss for farmers. Significant scientific and technological advances have been made in defect assessment, quality grading, disease recognition, pests, insects, fruits, and vegetable types recognition and evaluation of a wide range of agricultural plants, crops, leaves, and fruits. The book discusses intelligent robots developed with the touch of CV-ML which can help farmers to perform various tasks like planting, weeding, harvesting, plant health monitoring, and so on. The topics covered in the book include plant, leaf, and fruit disease detection, crop health monitoring, applications of robots in agriculture, precision farming, assessment of product quality and defects, pest, insect, fruits, and vegetable types recognition.
Optimizing Biofuel Production With Artificial Intelligence
DOWNLOAD
Author : Arindam Kuila
language : en
Publisher: John Wiley & Sons
Release Date : 2025-06-11
Optimizing Biofuel Production With Artificial Intelligence written by Arindam Kuila and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-11 with Computers categories.
Optimizing Biofuel Production with Artificial Intelligence will help readers discover how integrating artificial intelligence with biotechnological advancements can revolutionize biofuel production, ensuring a sustainable energy future in response to pressing global challenges like pollution and climate change. This book presents artificial intelligence as a technique to aid the production of biofuels. Recently, tremendous developments have been made in energy and environmental biotechnologies, spurred by societal issues like pollution control, energy security, and climate change. Energy can be obtained from a variety of sources, including coal, oil, natural gas, solar, wind, and nuclear energy. The need to transition to new energy results from finite resources and economic sustainability. Biotechnological process optimization is crucial for ensuring a quality final product and boosting bioconversion performance efficiency. When combined with traditional simulation and modeling methods, artificial intelligence and computer technology can help define ideal process parameters and save total process costs. The energy sector can benefit from artificial intelligence in several ways, including increased asset efficiency, early detection and assessment of wildfire risks, assistance with vegetation management and storm recovery, and optimized energy use. The new frontier for energy is biomass.
Artificial Intelligence For Industries Of The Future
DOWNLOAD
Author : Mayank Kejriwal
language : en
Publisher: Springer Nature
Release Date : 2022-11-25
Artificial Intelligence For Industries Of The Future written by Mayank Kejriwal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-25 with Business & Economics categories.
This book provides a brief synthesis of the known implementations, opportunities and challenges at the intersection of artificial intelligence (AI) and modern industry beyond the big-four companies that traditionally consume and produce such advanced technology: Facebook, Amazon, Microsoft and Google. With this information, the author also makes some reasonable claims about the role of AI in future industries. The book draws on a broad range of material, including reports from consulting firms, published surveys, academic papers and books, and expert knowledge available to the author due to numerous collaborations in academia and industry on AI. It is rigorous rather than speculative, drawing on known findings and expert summaries, where available. This provides industry leaders and other interested stakeholders with an accessible review of contemporary perspectives on AI’s forward-looking role in industry as well as a clarifying guide on the major issues that companies are likely to face as they commence on this exciting path. Examines the likely role of AI in industries of the future, both known and unknown Presents use-cases of AI currently being explored across Big Tech, multi-national corporations and start-ups Explores the regulation of AI and its potential impacts on the workforce
Sustainable Agriculture In The Era Of The Omics Revolution
DOWNLOAD
Author : Channa S. Prakash
language : en
Publisher: Springer Nature
Release Date : 2023-01-01
Sustainable Agriculture In The Era Of The Omics Revolution written by Channa S. Prakash and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-01 with Technology & Engineering categories.
Access to food with enough calories and nutrients is a fundamental right of every human. The global population has exceeded 7.8 billion and is expected to pass 10 billion by 2055. Such rapid population increase presents a great challenge for food supply. More grain production is needed to provide basic calories for humans. Thus, it is crucial to produce 60-110% more food to fill the gap between food production and the demand of future generations. Meanwhile food nutritional values are of increasing interest to accommodate industrialized modern lives. The instability of food production caused by global climate change presents another great challenge. The global warming rate has become more rapid in recent decades, with more frequent extreme climate change including higher temperatures, drought, and floods. Our world faces various unprecedented scenarios such as rising temperatures, which causes melting glaciers and the resulting various biotic and abiotic stresses, ultimately leading to food scarcity. In these circumstances it is of utmost importance to examine the genetic basis and extensive utilization of germplasm to develop “climate resilient cultivars” through the application of plant breeding and biotechnological tools. Future crops must adapt to these new and unpredictable environments. Crop varieties resistant to biotic and abiotic stresses are also needed as plant disease, insects, drought, high- and low-temperature stresses are expected to be impacted by climate change. Thus, we need a food production system that can simultaneously satisfy societal demands and long-term development. Since the Green Revolution in the 1960s, farming has been heavily dependent on high input of nitrogen and pesticides. This leads to environmental pollution which is not sustainable in the long run. Therefore, a new breeding scheme is urgently needed to enable sustainable agriculture; including new strategies to develop varieties and crops that have high yield potential, high yield stability, and superior grain quality and nutrition while also using less consumption of water, fertilizer, and chemicals in light of environmental protection. While we face these challenges, we also have great opportunities, especially with flourishing developments in omics technologies. High-quality reference genomes are becoming available for a larger number of species, with some species having more than one reference genome. The genome-wide re-sequencing of diverse varieties enables the identification of core- and pan-genomes. An integration of omics data will enable a rapid and high-throughput identification of many genes simultaneously for a relevant trait. This will change our current research paradigm fundamentally from single gene analysis to pathway or network analysis. This will also expand our understanding of crop domestication and improvement. In addition, with the knowledge gained from omics data, in combination with new technologies like targeted gene editing, we can breed new varieties and crops for sustainable agriculture.