Application of Machine Learning Models in Agricultural and Meteorological Sciences PDF Download

Author: Mohammad Ehteram
Publisher: Springer Nature
ISBN: 9811997330
Category : Computers
Languages : en
Pages : 201

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Book Description
This book is a comprehensive guide for agricultural and meteorological predictions. It presents advanced models for predicting target variables. The different details and conceptions in the modelling process are explained in this book. The models of the current book help better agriculture and irrigation management. The models of the current book are valuable for meteorological organizations. Meteorological and agricultural variables can be accurately estimated with this book's advanced models. Modelers, researchers, farmers, students, and scholars can use the new optimization algorithms and evolutionary machine learning to better plan and manage agriculture fields. Water companies and universities can use this book to develop agricultural and meteorological sciences. The details of the modeling process are explained in this book for modelers. Also this book introduces new and advanced models for predicting hydrological variables. Predicting hydrological variables help water resource planning and management. These models can monitor droughts to avoid water shortage. And this contents can be related to SDG6, clean water and sanitation. The book explains how modelers use evolutionary algorithms to develop machine learning models. The book presents the uncertainty concept in the modeling process. New methods are presented for comparing machine learning models in this book. Models presented in this book can be applied in different fields. Effective strategies are presented for agricultural and water management. The models presented in the book can be applied worldwide and used in any region of the world. The models of the current books are new and advanced. Also, the new optimization algorithms of the current book can be used for solving different and complex problems. This book can be used as a comprehensive handbook in the agricultural and meteorological sciences. This book explains the different levels of the modeling process for scholars.

Author: Mohammad Ayoub Khan
Publisher: Academic Press
ISBN: 0323906680
Category : Business & Economics
Languages : en
Pages : 332

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Book Description
Application of Machine Learning in Smart Agriculture is the first book to present a multidisciplinary look at how technology can not only improve agricultural output, but the economic efficiency of that output as well. Through a global lens, the book approaches the subject from a technical perspective, providing important knowledge and insights for effective and efficient implementation and utilization of machine learning. As artificial intelligence techniques are being used to increase yield through optimal planting, fertilizing, irrigation, and harvesting, these are only part of the complex picture which must also take into account the economic investment and its optimized return. The performance of machine learning models improves over time as the various mathematical and statistical models are proven. Presented in three parts, Application of Machine Learning in Smart Agriculture looks at the fundamentals of smart agriculture; the economics of the technology in the agricultural marketplace; and a diverse representation of the tools and techniques currently available, and in development. This book is an important resource for advanced level students and professionals working with artificial intelligence, internet of things, technology and agricultural economics. Addresses the technology of smart agriculture from a technical perspective Reveals opportunities for technology to improve and enhance not only yield and quality, but the economic value of a food crop Discusses physical instruments, simulations, sensors, and markets for machine learning in agriculture

Author: Hashmi, Mohamamd Farukh
Publisher: IGI Global
ISBN: 1668499762
Category : Technology & Engineering
Languages : en
Pages : 276

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Book Description
Machine Learning and Deep Learning for Smart Agriculture and Applications delves into the captivating realm of artificial intelligence and its pivotal role in transforming the landscape of modern agriculture. With a focus on precision agriculture, digital farming, and emerging concepts, this book illuminates the significance of sustainable food production and resource management in the face of evolving digital hardware and software technologies. Geospatial technology, robotics, the Internet of Things (IoT), and data analytics converge with machine learning and big data to unlock new possibilities in agricultural management. This book explores the synergy between these disciplines, offering cutting-edge insights into data-intensive processes within operational agricultural environments. From automated irrigation systems and agricultural drones for field analysis to crop monitoring and precision agriculture, the applications of machine learning are far-reaching. Animal identification and health monitoring also benefit from these advanced techniques. With practical case studies on vegetable and fruit leaf disease detection, drone-based agriculture, and the impact of pesticides on plants, this book provides a comprehensive understanding of the applications of machine learning and deep learning in smart agriculture. It also examines various modeling techniques employed in this field and showcases how artificial intelligence can revolutionize plant disease detection. This book serves as a comprehensive guide for researchers, practitioners, and students seeking to harness the power of AI in transforming the agricultural landscape.

Author: Ramesh Chandra Poonia
Publisher: Academic Press
ISBN: 0323903622
Category : Computers
Languages : en
Pages : 408

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Book Description
The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm. Introduces new deep learning models developed to address sustainable solutions for issues related to agriculture Provides reviews on the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and mitigation of sustainable agriculture Illustrates through case studies how deep learning has been used to address a variety of agricultural diseases that are currently on the cutting edge Delivers an accessible explanation of artificial intelligence algorithms, making it easier for the reader to implement or use them in their own agricultural domain

Author: Utku Kose
Publisher: CRC Press
ISBN: 1000644375
Category : Computers
Languages : en
Pages : 380

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Book Description
An essential resource work for understanding how to design and develop smart applications for present and future problems of the field of agriculture.— Dr. Deepak Gupta, Maharaja Agrasen Institute of Technology, Delhi, India As a result of the advances in Artificial Intelligence (AI), many aspects of daily life have been transformed by smart digital technology. Advanced intelligent algorithms can provide powerful solutions to real-world problems. Smart applications have become commonplace. All areas of life are being changed by smart tools developed to deal with complex issues challenging both humanity and the earth. Artificial Intelligence and Smart Agriculture Applications presents the latest smart agriculture applications developed across the globe. It covers a broad array of solutions using data science and AI to attack problems facing agriculture worldwide. Features: Application of drones and sensors in advanced farming A cloud-computing model for implementing smart agriculture Conversational AI for farmer's advisory communications Intelligent fuzzy logic to predict global warming’s effect on agriculture Machine learning algorithms for mapping soil macronutrient elements variability A smart IoT framework for soil fertility enhancement AI applications in pest management A model using Python for predicting rainfall The book examines not only present solutions but also potential future outcomes. It looks at the role of AI-based algorithms and the almost infinite combinations of variables for agricultural applications. Researchers, public and private sector representatives, agriculture scientists, and students can use this book to develop sustainable and solutions for smart agriculture. This book’s findings are especially important as the planet is facing unprecedented environmental challenges from over-farming and climate change due to global warming.

Author: Ashok N. Srivastava
Publisher: CRC Press
ISBN: 1315354462
Category : Computers
Languages : en
Pages : 354

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Book Description
From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.

Author: Utku Kose
Publisher: CRC Press
ISBN: 1000604373
Category : Computers
Languages : en
Pages : 291

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Book Description
This book was created with the intention of informing an international audience about the latest technological aspects for developing smart agricultural applications. As artificial intelligence (AI) takes the main role in this, the majority of the chapters are associated with the role of AI and data analytics components for better agricultural applications. The first two chapters provide alternative, wide reviews of the use of AI, robotics, and the Internet of Things as effective solutions to agricultural problems. The third chapter looks at the use of blockchain technology in smart agricultural scenarios. In the fourth chapter, a future view is provided of an Internet of Things-oriented sustainable agriculture. Next, the fifth chapter provides a governmental evaluation of advanced farming technologies, and the sixth chapter discusses the role of big data in smart agricultural applications. The role of the blockchain is evaluated in terms of an industrial view under the seventh chapter, and the eighth chapter provides a discussion of data mining and data extraction, which is essential for better further analysis by smart tools. The ninth chapter evaluates the use of machine learning in food processing and preservation, which is a critical issue for dealing with issues concerns regarding insufficient foud sources. The tenth chapter also discusses sustainability, and the eleventh chapter focuses on the problem of plant disease prediction, which is among the critical agricultural issues. Similarly, the twelfth chapter considers the use of deep learning for classifying plant diseases. Finally, the book ends with a look at cyber threats to farming automation in the thirteenth chapter and a case study of India for a better, smart, and sustainable agriculture in the fourteenth chapter. This book presents the most critical research topics of today’s smart agricultural applications and provides a valuable view for both technological knowledge and ability that will be helpful to academicians, scientists, students who are the future of science, and industrial practitioners who collaborate with academia.

Author: Taesam Lee
Publisher: Springer Nature
ISBN: 3030647773
Category : Science
Languages : en
Pages : 215

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Book Description
This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.

Author: Ignacio Rojas
Publisher: Springer Nature
ISBN: 3031430859
Category : Computers
Languages : en
Pages : 723

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Book Description
This two-volume set LNCS 14134 and LNCS 14135 constitutes the refereed proceedings of the 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, held in Ponta Delgada, Portugal, during June 19–21, 2023. The 108 full papers presented in this two-volume set were carefully reviewed and selected from 149 submissions. The papers in Part I are organized in topical sections on advanced topics in computational intelligence; advances in artificial neural networks; ANN HW-accelerators; applications of machine learning in biomedicine and healthcare; and applications of machine learning in time series analysis. The papers in Part II are organized in topical sections on deep learning and applications; deep learning applied to computer vision and robotics; general applications of artificial intelligence; interaction with neural systems in both health and disease; machine learning for 4.0 industry solutions; neural networks in chemistry and material characterization; ordinal classification; real world applications of BCI systems; and spiking neural networks: applications and algorithms.

Author: Mohammad Shorif Uddin
Publisher: Springer Nature
ISBN: 9811699917
Category : Technology & Engineering
Languages : en
Pages : 269

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Book Description
This book is as an extension of previous book “Computer Vision and Machine Learning in Agriculture” for academicians, researchers, and professionals interested in solving the problems of agricultural plants and products for boosting production by rendering the advanced machine learning including deep learning tools and techniques to computer vision algorithms. The book contains 15 chapters. The first three chapters are devoted to crops harvesting, weed, and multi-class crops detection with the help of robots and UAVs through machine learning and deep learning algorithms for smart agriculture. Next, two chapters describe agricultural data retrievals and data collections. Chapters 6, 7, 8 and 9 focuses on yield estimation, crop maturity detection, agri-food product quality assessment, and medicinal plant recognition, respectively. The remaining six chapters concentrates on optimized disease recognition through computer vision-based machine and deep learning strategies.