Since agriculture is one of the key parameters in assessing the gross domestic product (GDP) of any country, it has become crucial to transition from traditional agricultural practices to smart agriculture. New agricultural technologies provide numerous opportunities to maximize crop yield by recognizing and analyzing diseases and other natural variables that may affect it. Therefore, it is necessary to understand how computer-assisted technologies can best be utilized and adopted in the conversion to smart agriculture. Modern Techniques for Agricultural Disease Management and Crop Yield Prediction is an essential publication that widens the spectrum of computational methods that can aid in agriculture disease management, weed detection, and crop yield prediction. Featuring coverage on a wide range of topics such as soil and crop sensors, swarm robotics, and weed detection, this book is ideally designed for environmentalists, farmers, botanists, agricultural engineers, computer engineers, scientists, researchers, practitioners, and students seeking current research on technology and techniques for agricultural diseases and predictive trends.

This book provides a comprehensive overview of organic fertilizers and their importance in sustainable agriculture, biodiversity, and the environment. It presents new approaches, ideas, and trends on how to increase the effectiveness of chemical fertilizers as well as the resistance of plants against biotic and abiotic stress conditions. Chapters address such topics as the benefits of organic fertilizers over their chemical counterparts, vermicomposting, organic farming, insects in organic fertilizer production, and much more.

Promoting rural entrepreneurship is a necessary step to limit the negative effects of classical agricultural policy based on a linear process and attracting secondary resources to the economic process. The analysis of agricultural policy and rural development in conjunction to entrepreneurship in terms of production may represent a further step in understanding the role and importance of diversifying the rural potentials in contemporary economies. The Handbook of Research on Agricultural Policy, Rural Development, and Entrepreneurship in Contemporary Economies is an essential publication of academic research that examines agricultural policy and its impact on shaping future resilient economy in rural areas and identifies green business models and new business patterns in rural communities. Covering a range of topics such as entrepreneurship, product management, and marketing, this book is ideal for researchers, policymakers, academicians, economists, agriculture professionals, rural developers, business investors, and students.

This book proposes new technologies and discusses future solutions for ICT design infrastructures, as reflected in high-quality papers presented at the 5th International Conference on ICT for Sustainable Development (ICT4SD 2020), held in Goa, India, on 23–24 July 2020. The conference provided a valuable forum for cutting-edge research discussions among pioneering researchers, scientists, industrial engineers, and students from all around the world. Bringing together experts from different countries, the book explores a range of central issues from an international perspective.

This new book, Plant Diseases and Their Management: A Sustainable Approach, studies the most modern methods in control and management of plant diseases. It covers a wide range of themes on the biological, cultural, chemical, and genome engineering controls for plant diseases brought on by viruses, bacteria, phytoplasma, and fungi. This book details how natural materials, organic disease control, and new-generation fungicides can all be utilized to thwart or stop plant pathogen activity in an effective manner. The book also delves into methods for increasing the shelf life of produce, presents approaches to plant disease management in organic as well as conventional farming, and considers molecular approaches to disease detection and identification in plants. The book looks at viral, bacterial, and fungal diseases in different plants and their management. It also discusses several pathogens and how diseases caused by these can be managed effectively. It also covers diseases in specific crops, such as rice, pulses, fruits, and vegetables, including apples, berries, and capsicum. A novel approach of genome engineering to develop resilience in plants against various diseases and future challenges is considered as well. Key features: Presents management approaches to fungal, phytoplasmal, viral, and bacterial plant diseases Discusses the protection of fruits, vegetables, and crops from various diseases for prolonged shelf-life Looks at genome engineering as a novel approach for fungal, bacterial, and viral disease management Considers both traditional and modern methods in the management of viruses infecting plants Covering new methods for the sustainable control of plant diseases, this volume will be valuable to plant and crop specialists, agriculture-based industries, and faculty and students in the agricultural sciences.

This book constitutes the proceedings of the 13th International Conference on Intelligent Robotics and Applications, ICIRA 2020, held in Kuala Lumpur, Malaysia, in November 2020. The 45 full papers and 3 short papers were carefully reviewed and selected from 66 submissions. The accepted papers were grouped into various subtopics including Advanced Measurement and Machine Vision System; Automation; Human-Robot Interaction; Mobile Robots and Intelligent Autonomous System; Recent Trends in Computational Intelligence; Robot Design, and Development and Control. Due to the Corona pandemic ICIRA 2020 was held as a virtual event.

Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.