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Provides a comprehensive review of the recent advances in agricultural robotics, such as advances in sensing and perception, as well as technologies and actuation Addresses our understanding of the social, ethical and economic aspects of agricultural robotics, including the regulatory frameworks and standards required to authorise their adoption Provides examples of the practical application of agricultural robotics in an array of agricultural settings, from greenhouse and orchard cultivation, to meat/fish processing
Navigation and path planning are essential technologies for increasing the productivity of agriculture machine systems performing modern precision agriculture tasks. Production agriculture requires efficient methods for complete coverage of agricultural landscapes to complete the critical production steps of preparing the land and planting, managing, and harvesting crops. To help farmers to make the transformation from automated to autonomous systems requires approaches that can leverage the current automation advances from modern precision agricultural machinery and build on them as tools in the development and deployment of agricultural robots. This chapter provides a high-level overview of critical elements in autonomous navigation and path planning and discusses the opportunities and challenges related to building on precision agriculture technologies to enable productive agricultural robots.
Robotics and automation face several challenges in agriculture due to the high variability of products, task complexity, crop quality requirement, and dense vegetation. Such a set of challenges demands a more versatile and safe robotic system. Soft robotics is a new yet promising field of research aimed at enhancing current rigid robots, making it a good potential solution for these challenges. In this chapter, we review a potential group of soft grippers used for handling and harvesting crops and their suitability for agriculture, including challenges such as safety in handling and adaptability to different crops. The review aims to show why and to what extent soft grippers have been successful in handling agricultural tasks. This analysis provides a framework for future, systematic design of soft robots for a range of agricultural tasks.
In the current chapter, we aim to present an overview of the advantages and limitations of UAV-RS platforms and their applications in PA. To properly review these applications, we formulated the following questions: What are the available UAV sensors for PA applications? How should one select the best UAV platform for different PA tasks? What needs to be considered in flight planning and in performing the necessary pre-processing to obtain high data quality? What are the main applications of UAV-RS in PA? Finally, we will discuss the future applications and challenges that lie ahead.
In recent years, there has been growing interest in industrial systems, especially in robotic manipulators and mobile robot systems. As the cost of robots goes down and become more compact, the number of industrial applications of robotic systems increases. Moreover, there is need to design industrial systems with intelligence, autonomous decision making capabilities, and self-diagnosing properties. Intelligent Industrial Systems: Modeling, Automation and Adaptive Behavior analyzes current trends in industrial systems design, such as intelligent, industrial, and mobile robotics, complex electromechanical systems, fault diagnosis and avoidance of critical conditions, optimization, and adaptive behavior. This book discusses examples from major areas of research for engineers and researchers, providing an extensive background on robotics and industrial systems with intelligence, autonomy, and adaptive behavior giving emphasis to industrial systems design.
This chapter reviews advances in the use of robotics in crop phenotyping. It first highlights the role of robotics in phenotyping, then moves on to discuss three forms of dimensional imaging and analysis: two dimensional (2D), 3D and 4D. A section is dedicated to each. The chapter also provides two case studies, the first focuses on models to detect abiotic stress in corn plants using spectral reflectance and hyperspectral images of plant leaves. The second case study draws attention to biotic stress and compares leaf point spectra and whole plant hyperspectral images in the early detection of Fusarium infection in corn plants.
This handbook incorporates new developments in automation. It also presents a widespread and well-structured conglomeration of new emerging application areas, such as medical systems and health, transportation, security and maintenance, service, construction and retail as well as production or logistics. The handbook is not only an ideal resource for automation experts but also for people new to this expanding field.
Introduction of robotics into agriculture has the potential to lower production costs, reduce the drudgery of tedious manual labor, increase the level of accuracy of mechanized operations, and improve environmental control. Unlike industrial applications that often deal with relatively simple, repetitive, well-defined, and known a priori tasks, agriculture robots usually require advanced technologies to deal with the relatively more complex and highly unstructured and dynamic nature of both biological produce and the environment. This chapter reviews agricultural robotic systems with a focus on recent advances in human–robot collaboration.
Provides a comprehensive overview of the key concepts in biodiversity management within agricultural landscapes Considers the role of farmers and rural communities in implementing ecological restoration practices Reviews the importance of habitat and animal rewilding in promoting biodiversity and other key ecosystem services