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Books in Information technology in agriculture

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    • Machine Learning and AI Technology for Agricultural Applications

      • 1st Edition
      • Kishore Chandra Swain + 5 more
      • English
      Machine Learning and AI Technology in Agricultural Applications offers a comprehensive overview of how artificial intelligence and machine learning are transforming the agricultural industry. By delving into image processing and advanced data analysis, the book demonstrates how technology addresses modern agricultural challenges, including climate change, urbanization, and increasing global populations. It emphasizes the importance of integrating sensors and data collection methods to generate vast pools of information, which can be efficiently analyzed through AI-driven solutions. The text lays a strong foundation for understanding the role of technological innovation in supporting sustainable and secure food production.Beyond introducing core machine learning models such as random forest, support vector machines, logistic regression, and decision trees, the book highlights the centralization of critical agricultural data in the cloud. This resource benefits both students and seasoned agricultural scientists, providing practical insights for optimizing crop yields, monitoring soil and weather conditions, and managing resources like fertilizers and pesticides. The book also explores the rapid analysis of complex datasets, empowering users to make informed, timely decisions in real-world agricultural scenarios.

    • Agricultural Insights from Space

      Machine Learning Applications in Satellite Data Analysis
      • 1st Edition
      • Dharmendra Singh + 2 more
      • English
      Agricultural Insights from Space offers a comprehensive exploration of how geospatial technology and machine learning are transforming modern agriculture. From satellite data acquisition and soil mapping to crop classification, yield prediction, and irrigation optimization, this volume presents cutting-edge methods for advancing precision and sustainable farming.Key chapters highlight the integration of spatial data with AI to monitor crop health, track pest and disease outbreaks, manage livestock, and map agroforestry systems. The use of climate data and deep learning models illustrates how these innovations strengthen resilience and support informed decision-making in the face of environmental challenges.Through detailed methodologies and real-world case studies, including applications of Lagrange polynomials, deep learning ensembles, and synthetic data generation, the book showcases practical solutions that bridge research and implementation.Wheth... applied in academic research, fieldwork, or technology development, Agricultural Insights from Space offers a multidisciplinary foundation for tackling complex agricultural challenges. It empowers readers to harness emerging technologies not just to improve efficiency, but to reshape agricultural systems for long-term sustainability and impact.

    • Agri 4.0 and the Future of Cyber-Physical Agricultural Systems

      • 1st Edition
      • Seifedine Kadry + 4 more
      • English
      Agri 4.0 and the Future of Cyber-Physical Agricultural Systems is the first book to explore the potential use of technology in agriculture with a focus on technologies that enable the reader to better comprehend the full range of CPS opportunities. From planning to distribution, CPS technologies are available to impact agricultural output, delivery, and consumption. Specific sections explore ways to implement CPS effectively and appropriately and cover digitalization of agriculture, digital computers to assist the processes of agriculture with digitized data and allied technologies, including AI, Computer Vision, Big data, Block chain, and IoT. Other sections cover Agri 4.0 and how it can digitalize, estimate, plan, predict, and produce the optimum agricultural inputs and outputs required for commercial purposes. The global team of authors also presents important insights into promising areas of precision agriculture, autonomous systems, smart farming environment, smart production monitoring, pest detection and recovery, sustainable industrial practices, and government policies in Agri 4.0.

    • Unmanned Aerial Systems in Agriculture

      Eyes Above Fields
      • 1st Edition
      • Dionysis Bochtis + 2 more
      • English
      Unmanned Aerial Systems in Agriculture: Eyes Above Fields bridges the gap between knowledge of concept and real-world use and operations of UASs in agri-production. Based on a valuable combination of themes presented at the 13th European Federation for Information Technology in Agriculture, Food and the Environment (EFITA) and supplemented by targeted invited articles of key-scientists, this book presents a full-spectrum view of the use of unmanned aerial systems (UAS) for agricultural applications. It integrates dispersed knowledge in the field, providing a holistic approach regarding UAVs and other UAS and their use in sustainable decisions. The integrated approach of the book provides a fresh look on contemporary agriculture-related issues, following precision farming approaches, by educating on a range of different issues of remote sensing and its use in agriculture. Furthermore, the operational planning aspects for UAS in agriculture focus part of the book provides information that is missing from other resources.

    • Application of Machine Learning in Agriculture

      • 1st Edition
      • Mohammad Ayoub Khan + 2 more
      • English
      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.

    • Intelligent Data Mining and Fusion Systems in Agriculture

      • 1st Edition
      • Xanthoula-Eirini Pantazi + 2 more
      • English
      Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. Sections cover the combination of sensors with artificial intelligence architectures in precision agriculture, including algorithms, bio-inspired hierarchical neural maps, and novelty detection algorithms capable of detecting sudden changes in different conditions. This book offers advanced students and entry-level professionals in agricultural science and engineering, geography and geoinformation science an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms.

    • Federal Data Science

      Transforming Government and Agricultural Policy Using Artificial Intelligence
      • 1st Edition
      • Feras A. Batarseh + 1 more
      • English
      Federal Data Science serves as a guide for federal software engineers, government analysts, economists, researchers, data scientists, and engineering managers in deploying data analytics methods to governmental processes. Driven by open government (2009) and big data (2012) initiatives, federal agencies have a serious need to implement intelligent data management methods, share their data, and deploy advanced analytics to their processes. Using federal data for reactive decision making is not sufficient anymore, intelligent data systems allow for proactive activities that lead to benefits such as: improved citizen services, higher accountability, reduced delivery inefficiencies, lower costs, enhanced national insights, and better policy making. No other government-dedicated work has been found in literature that addresses this broad topic. This book provides multiple use-cases, describes federal data science benefits, and fills the gap in this critical and timely area. Written and reviewed by academics, industry experts, and federal analysts, the problems and challenges of developing data systems for government agencies is presented by actual developers, designers, and users of those systems, providing a unique and valuable real-world perspective.