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In the dynamic realm of agriculture, artificial intelligence (AI) and machine learning (ML) emerge as catalysts for unprecedented transformation and growth. The emergence of big data, Internet of Things (IoT) sensors, and advanced analytics has opened up new possibilities for farmers to collect and analyze data in real-time, make informed decisions, and increase efficiency. AI and ML are key enablers of data-driven farming, allowing farmers to use algorithms and predictive models to gain insights into crop health, soil quality, weather patterns, and more. Agriculture is an industry that is deeply rooted in tradition, but the landscape is rapidly changing with the emergence of new technologies. Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture is a comprehensive guide that explores how the latest advances in technology can help farmers make better decisions and maximize yields. It offers a detailed overview of the intersection of data, AI, and ML in agriculture and offers real-world examples and case studies that demonstrate how these tools can help farmers improve efficiency, reduce waste, and increase profitability. Exploring how AI and ML can be used to achieve sustainable and profitable farming practices, the book provides an introduction to the basics of data-driven farming, including an overview of the key concepts, tools, and technologies. It also discusses the challenges and opportunities facing farmers in today’s data-driven landscape. Covering such topics as crop monitoring, weather forecasting, pest management, and soil health management, the book focuses on analyzing data, predicting outcomes, and optimizing decision-making in a range of agricultural contexts.
This report aims to identify the different scenarios where the process of digital transformation is taking place in agriculture. This identifies those aspects of basic conditions, such as those of infrastructure and networks, affordability, education and institutional support. In addition, enablers are identified, which are the factors that allow adopting and integrating changes in the production and decision-making processes. Finally identify through cases, existing literature and reports how substantive changes are taking place in the adoption of digital technologies in agriculture.
The digital agriculture revolution holds a promise to build an agriculture and food system that is efficient, environmentally sustainable, and equitable, one that can help deliver the Sustainable Development Goals. Unlike past technological revolutions in agriculture, which began on farms, the current revolution is being sparked at multiple points along the agrifood value chain. The change is driven by the ability to collect, use, and analyze massive amounts of machine-readable data about practically every aspect of the value chain, and by the emergence of digital platforms disrupting existing business models. All this allows for drastically reduced transaction costs and pervasive information asymmetries that plague the agrifood system. The success of the digital transformation, however, is not guaranteed as the risks it brings are numerous, including those related to data governance and inadequate competition within and between digital platforms. What’s Cooking: Digital Transformation of the Agrifood System investigates how digital technologies can accelerate the transformation of the agrifood system by increasing efficiency on the farm; improving farmers’ access to output, input, and financial markets; strengthening quality control and traceability; and improving the design and delivery of agriculture policies. It also identifies a key role for the public sector in maximizing the benefits of this process while minimizing its risks, through enabling an innovation ecosystem featuring open datasets, digital platforms, digital entrepreneurship, digital payment systems, and digital skills and encouraging equitable technology adoption.
This book focuses on the recent advances in precision agriculture and satellite farming, detailing applications for sensing, data handling, modeling, and control. In addition, the book reviews its history - establishing the background on the various processes and applications – describes the current status, and offers insight into the future technology of satellite farming in India. Introducing processes and applications based on a global scale, the book reveals how precision agriculture can be used in large-scale agriculture, community agriculture, and diversified farming. It includes site-specific information from a variety of information sources for planning, planting, growing, and harvesting agricultural crops. It also presents a new concept based on the control system theory that can be used to formulate systematic methods for more effective precision crop production. Precision agriculture when properly integrated into the crop production process, can greatly improve overall production and sustainability.
Faced with challenges of resource scarcity and environmental degradation, it is important to adopt innovative farming systems that maximize resource efficiency while protecting the environment. Soil-Specific Farming: Precision Agriculture focuses on principles and applications of soil-specific farming, providing information on rapidly evolving agri
The research in this book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers' choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models are utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies. Technical topics discussed in the book include: Precision agriculture Machine learning Wireless sensor networks IoT Deep learning
As technology continues to saturate modern society, agriculture has started to adopt digital computing and data-driven innovations. This emergence of “smart” farming has led to various advancements in the field, including autonomous equipment and the collection of climate, livestock, and plant data. As connectivity and data management continue to revolutionize the farming industry, empirical research is a necessity for understanding these technological developments. Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture provides emerging research exploring the theoretical and practical aspects of critical technological solutions within the farming industry. Featuring coverage on a broad range of topics such as crop monitoring, precision livestock farming, and agronomic data processing, this book is ideally designed for farmers, agriculturalists, product managers, farm holders, manufacturers, equipment suppliers, industrialists, governmental professionals, researchers, academicians, and students seeking current research on technological applications within agriculture and farming.
With the growing popularity and availability of precision equipment, farmers and producers have access to more data than ever before. With proper implementation, precision agriculture management can improve profitability and sustainability of production. Precision Agriculture Basics is geared at students, crop consultants, farmers, extension workers, and practitioners that are interested in practical applications of site-specific agricultural management. Using a multidisciplinary approach, readers are taught to make data-driven on-farm decisions using the most current knowledge and tools in crop science, agricultural engineering, and geostatistics. Precision Agriculture Basics also features a stunning video glossary including interviews with agronomists on the job and in the field.
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.
The study presented in this report was commissioned by the Technical Centre for Agricultural and Rural Cooperation (CTA) as a member of the Global Open Data for Agriculture & Nutrition (GODAN) initiative, and was conducted by SB Consulting (SBC4D). The objective of the research is to understand the role of farmer organisations (FO) and cooperatives in the agriculture data ecosystem. These organisations have long been recognised to play an important role in society that translates into the improvement of living conditions of their members, particularly the low-income earning population. More than 40% of households in Africa are member of a cooperative society ([ILO-2000]) and the cooperative movement is Africa’s biggest nongovernmental organisation. The key question this report explores is the role of these organisations in the emergent “data revolution.” How can they ensure that this data revolution benefits their members and the smallholder farmers in general, and at the same time contribute to the revolution by providing valuable information to policy makers or other stakeholders of the ecosystem?