Spore 195 Smart Farming Transforming Agriculture With Artificial Intelligence

Transforming Agriculture with Artificial Intelligence. At a time where the world needs to produce more with fewer resources, artificial intelligence (AI) could help to transform agriculture worldwide. SPORE is the quarterly magazine of the Technical Centre for Agricultural and Rural Cooperation (CTA), offering a global perspective on agribusiness and sustainable agriculture. CTA operates under the Cotonou Agreement between the countries of the Africa, Caribbean and Pacific (ACP) group and the European Union and is financed by the EU.

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.

This book addresses the challenges for developing and emerging trends in Internet-of-Things (IoT) for smart agriculture platforms. It also describes data analytics & machine learning, cloud architecture, automation & robotics and aims to overcome existing barriers for smart agriculture with commercial viability. It discusses IoT-based monitoring systems for analyzing the crop environment, and methods for improving the efficiency of decision-making based on the analysis of harvest statistics. The book explores a range of applications including intelligent field monitoring, intelligent data processing and sensor technologies, predictive analysis systems, crop monitoring, and weather data-enabled analysis in IoT agro-systems. This volume will be helpful for engineering and technology experts and researchers, as well as for policy-makers.

Digital Agricultural Ecosystem The book comprehensively explores the dynamic synergy between modern technology and agriculture, showcasing how advancements such as artificial intelligence, data analytics, and smart farming practices are reshaping the landscape to ensure food security in the era of climate change, as well as bridging the gap between cutting-edge research and practical implementation. Agriculture has historically been the foundation of human civilization and benefits communities all around the world. Agriculture has a creative, adaptable, and innovative history, and as the digital age draws closer, agriculture is once again poised for change. Each of the 20 chapters explores the connection between agricultural and technological advancements, and are divided into four key areas. Part 1 covers knowledge sharing in the digital agricultural ecosystem. In the context of modern agriculture, the chapters underscore the importance of information flow. Through comprehensive reviews of literature and assessments of farmer participation on social media platforms, these chapters illustrate the value of information sharing for sustainable agriculture. Part 2 explores the adoption and impact of digital technologies in agriculture. The use of cutting-edge digital technologies in agriculture is examined thoroughly in this section. The chapters included here outline how precision, artificial intelligence, and blockchain technology have the potential to transform methods of agriculture and improve food systems. Part 3 addresses smart farming and sustainable agriculture. This section focuses on sustainability and offers details on eco-friendly production methods, the significance of smart farming in many nations, including India and the UK, and cost-effective fertilizer sprayer technologies. Part 4 examines the modeling and analysis of agricultural systems. This last section explores how mathematical modeling and data analytics are used in agricultural systems, with insights on everything from the study of credit access constraints in rural regions to water resource management in irrigation systems. Audience The diverse readership includes farmers, agronomists, agricultural researchers, policymakers, environmentalists, information technologists, and students from academic and professional fields who are eager to learn more about how digital innovation and sustainable agriculture can be used to address global issues such as climate change, food security, and smart farming.

The book discusses one of the major challenges in agriculture which is delivery of cultivate produce to the end consumers with best possible price and quality. Currently all over the world, it is found that around 50% of the farm produce never reaches the end consumer due to wastage and suboptimal prices. The authors present solutions to reduce the transport cost, predictability of prices on the past data analytics and the current market conditions, and number of middle hops and agents between the farmer and the end consumer using IoT-based solutions. Again, the demand by consumption of agricultural products could be predicted quantitatively; however, the variation of harvest and production by the change of farm's cultivated area, weather change, disease and insect damage, etc., could be difficult to be predicted, so that the supply and demand of agricultural products has not been controlled properly. To overcome, this edited book designed the IoT-based monitoring system to analyze crop environment and the method to improve the efficiency of decision making by analyzing harvest statistics. The book is also useful for academicians working in the areas of climate changes.

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.

This book in its eighteen chapters provides sustainable and smart technology-based solutions to developing nations. Smart farming is growing in importance due to the increasing demand for higher crop yield, the need for using natural resources efficiently, the rising use and sophistication of information and communication technology and the increasing need for climate-smart agriculture. Some of these chapters deal with the applications of smart agriculture to address the special challenges of the Small Island Developing States for sustainable agriculture. The book brings together scientific communities from India, Indonesia, Iraq, Malaysia, Mauritius, Myanmar, Palestine, Sri Lanka and South Africa to share their expertise to make the farming system more connected and intelligent. The smart farming techniques shared in different chapters not only help to reduce the overall cost and improve the quality and quantity of products but altogether help improve the sustainability of agriculture in the developing world. ‘Smart Farming’ is a management concept focused on providing the agricultural industry with the infrastructure to leverage advanced technologies—including artificial intelligence (AI), big data, robotics, drones and Internet of Things (IoT)—for tracking, monitoring, automating and analyzing operations. Sometimes known as precision agriculture, smart farming is software managed and sensor monitored.

This book endeavours to highlight the untapped potential of Smart Agriculture for the innovation and expansion of the agriculture sector. The sector shall make incremental progress as it learns from associations between data over time through Artificial Intelligence, deep learning and Internet of Things applications. The farming industry and Smart agriculture develop from the stringent limits imposed by a farm's location, which in turn has a series of related effects with respect to supply chain management, food availability, biodiversity, farmers' decision-making and insurance, and environmental concerns among others. All of the above-mentioned aspects will derive substantial benefits from the implementation of a data-driven approach under the condition that the systems, tools and techniques to be used have been designed to handle the volume and variety of the data to be gathered. Contributions to this book have been solicited with the goal of uncovering the possibilities of engaging agriculture with equipped and effective profound learning algorithms. Most agricultural research centres are already adopting Internet of Things for the monitoring of a wide range of farm services, and there are significant opportunities for agriculture administration through the effective implementation of Machine Learning, Deep Learning, Big Data and IoT structures.