Agri 4.0 and the Future of Cyber-Physical Agricultural Systems

By Seifedine Kadry

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 the focus on the technologies, enabling 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. The impact for food security may be significant and this book explores ways to implement CPS effectively and appropriately.

Technology, especially computing technology, can play a significant in the field of agriculture by processing digitized data to solve the complex agronomic, agricultural demand and supply issues that impact the food supply chain, and ultimately food security. In Agri 4.0, the cyber physical system synchronously interacts with agricultural systems to control and execute the operation autonomously. Digitalization of agriculture integrates digital computers to assist the processes of agriculture with its digitized data and its allied technology including AI, Computer Vision, Big data, Block chain and IoT. Agri 4.0 digitalizes, estimate, plan, predict, and produce the optimum agricultural inputs and outputs for the required for commercial purposes. It can be used to get a fair, transparent and accountable process to serve the stakeholders. The convergence of IoT, ML, Big data and 5G networks have opened new possibilities to explore and exploit the cyber physical agricultural systems. The management and practices of smart multi-layer architecture and smart supply chain are one of the key application areas in Agri 4.0.

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.

• Addresses one of the most complex applications of CPS
• Describes various technologies, covering CPS in agriculture from precision agriculture to smart supply chain management
• Focuses on the digital framework, tools, and systems capable of supporting Agri 4.0

• Language: English
• Publisher: Academic Press
• Release date: Apr 16, 2024
• ISBN: 9780443131868

Book preview

Chapter 1

Journey to cyber-physical agricultural systems digitalization and technological evolution

Farzana Tasneem M.I. and Punitha V. Achar,    Department of Biotechnology, Surana College, Bangalore, Karnataka, India

Abstract

Digital technologies are pervasive, portable, and mobile, and they are revolutionizing agricultural and food production. There is no denying that the agricultural sector is undergoing a digital transformation as mobile technologies, remote sensing services, and distributed computing are already improving smallholders’ access to information, inputs, and markets, increasing manufacturing and productivity, smooth supply chains, and embracing technology costs.

The Indian economy heavily depends on agriculture. Over 60% of Indians work in agriculture, which also accounts for one-third of the nation’s income and contributes significantly to the development of the nation. Digitalization has had an impact on agricultural and food production systems, allowing for the use of technologies and advanced data processing techniques in the agricultural field. The goal is to provide a comprehensive understanding of the various effects of digital technologies on society through a framework that aims to provide a deeper understanding of the relationship between the physical, cyber and social strategies for successfully implementing the digital transformation of a system. Using an agriculture cyber-physical systems makes agriculture smarter, which connect Internet of Things with other technologies like artificial intelligence (AI) and machine learning can aid in boosting crop yields, decreasing water waste, and lowering fertilizer usage, a range of agricultural factors that have a direct impact on crop selection. Second, it transmits this data to a server that uses it to forecast farm-ready yields. Digital farming aims to solve several existing challenges in food security, climate protection, and resource management by utilizing available information from agricultural assets. The use of digital techniques is anticipated to increase optimization and decision-making support. It covers a broad framework of digital twins in agricultural fields, including soil, irrigation, robotics, farm equipment, and food postharvest processing. Data collection, modeling, including big data, AI, simulation, analysis, and prediction as well as communication components of the digital twin in agriculture are explored. As the next phase of the digitalization paradigm, digital twin technologies can assist farmers by continuously and in real-time monitoring the physical world (the farm) and updating the state of the virtual world. The potential of digital technologies to transform the agricultural and rural sectors is often seen as a promising opportunity in various aspects of society.

Keywords

Digitalization; agriculture; cyber-physical system; digital twin technology

1.1 Introduction of agricultural cyber-physical system

A new paradigm for agricultural production and management is being developed using the agricultural cyber-physical system (ACPS), a framework that applies scientific ideas, engineering design techniques, and digital technology. Digital technologies have been used to secure, monitor, and support decision-making in the ACPS. The goal of ACPS is to offer an integrated platform for the various applications in agriculture. This book will address the opportunities and difficulties of this framework from the viewpoints of both farmers and researchers. Digital technologies have been used to secure, monitor, and support decision-making in the ACPS. One of the most significant businesses in the world, agriculture must continue to develop to remain competitive and boost the sustainability of its food production. To make agriculture a vital industry in terms of digitalization, automation, and integration with other socioeconomic sectors, we must make the most of all the opportunities offered by cutting-edge technologies.

A consistent access to more information, communication, and processing capacity, the cyber-physical agricultural system is a cutting-edge attempt to raise the overall efficiency, sustainability, and competitiveness of the agricultural industry. The purpose of this study is to determine how digitalization has affected agricultural productivity by analyzing the knowledge base using metagenerative methods. The goal is to construct a conceptual model that will allow us to mimic a smart system in a virtual reality environment by using unique knowledge representations and processes. The creation of a cyber-physical agricultural system contrasts with conventional conceptions of cybernetics and automation research. Strategy is a component of a new method for building complex systems that relies on distributed networks acting as nodes for internet connectivity and cellular superconducting communications platforms connecting them to carry out industrial processes like climate control and agricultural production.

In today’s technologically advanced world, most farmers struggle because they lack reliable information on farming and gardening. Most activities related to farming and cultivation rely on expectation and anticipation. When it fails, the farmer is forced to take significant losses. With diminishing resources, shrinking land sizes, rising input and labor expenses, and the unpredictability of numerous elements such as weather and market pricing, agriculture in India has evolved into a dangerous profession. Technology breakthroughs need to be addressed by a few professions because they have already led to significant gains in several fields. Contrarily, agriculture has not benefited from these improvements. With this initiative, we hope to improve at least one step in the agricultural process—choosing the right crop for the farm. Farmers typically choose their crops based on a variety of factors, such as market demand, and out of convenience, they choose the crop that yields the highest profit and requires the least amount of investment. This leads to the issue of monoculture. Growing one plant species over a wide area of land is known as monoculture. Farmers now go to land that only produces one sort of crop, rather than producing a variety of crops as they have done for the most of human history (Fig. 1.1).

Figure 1.1 Cyber-physical agricultural system.

1.2 Digitalization

In society, agriculture is a significant industry. It contributes significantly to GDP by generating jobs and social benefits. Agriculture will take longer to completely adopt digitalization because it is less developed than services. ACPSs are designed to build a productive and linked network of agricultural users, services, and supply chains. They are intended to enhance the value chain of digitalization by fully utilizing the benefits of connection, automation, and data access. CPSs can be used to monitor and control specific operations in real time or to automate entire processes. Agriculture’s daily activities are supported by ACPSs, which serve as the industry’s technical infrastructure. They support farmers in storing and managing their data, enhancing production efficiency, adopting new technologies that provide greater returns, and lowering agricultural and societal hazards. The process of going digital involves using information systems more frequently to boost productivity and decision-making. An integrated network of technology, models, and support services called the ACPS enables farmers to increase production, make the best use of resources, and lower risks related to their commercial activities.

Farmers can save and use vital information about their crops and soil more easily thanks to CPSs. They give farmers knowledge that enables them to care for their soil more effectively, which boosts agricultural yields and efficiency. Agribusinesses, governments, and farmers can all work more effectively and efficiently thanks to ACPSs which are CPSs that are founded in digital data. ACPS is an emerging technology where productivity is exponentially improved using intelligent tools and technologies. Growing approaches enabled by remote data analytics, automated decision-making, and real-time process control will be the primary benefit for farmers. For ACPSs to take use of the Internet of Things (IoT) and perform better, particularly in terms of resource management, new strategies are needed. The objective of this project is to create a smart crop management system that will conserve water, lower expenses, and improve the effectiveness of maintaining agricultural machinery.

1.3 Technological evolution

Technology for ACPSs is a new field in technological development. ACPSs are concerned with agricultural technology and how it is used to produce crops to meet the rising global demand for food. ACPSs are composed of a few different components. They start by bringing together every sort of technology used in agriculture and connecting them all over the internet. This makes it possible for farmers to make use of the most recent data regarding weather patterns, crop productivity, harvest dates, and other future forecasts that can help them run more efficiently. They make use of artificial intelligence (AI) programs that provide quick replies and real-time data for all farmers in a region by combining all these complex components into a sophisticated programmable platform that runs around-the-clock, 365 days a year. An information-gathering network of sensors that also controls equipment makes up an ACPS. The CPS collects information from weather balloons, satellites, and satellite-based sources such as air pressure and crop health. The science and technology behind crop planning, production, and harvesting is known as agriculture. The rearing of livestock and the upkeep of farm animals are also included. Agriculture’s advancement made a significant impact on human civilization.

1.4 Internet of Things

The networking of physical objects with electronics integrated into their design that enable them to communicate and interact with one another as well as with the outside world is known as the IoT. In the upcoming years, IoT-based technologies will supply advanced degrees of services, significantly altering how people live. Among the many industries where IoT is well-established are healthcare, energy, gene treatments, smart cities, and smart homes. Every facet of the common man’s life has been altered by the IoT which has made everything smart and intelligent, network of objects that can set itself up (Khairnar et al., 2022) (Fig. 1.2).

Figure 1.2 IoT, Internet of Things.

1.5 Digital farming

The use of technology by farmers to integrate financial and field-level records for full farm activity management is known as digital farming. To reduce losses and maximize the production of each plot on the farm, data from each plot can be examined to provide knowledge on the soil, weather, crop development trends, and deliver immediate, actionable spatially relevant insights. Through applications on their phones, farmers may even get their questions answered and directly manage the supply chain. Digital farming intends to take over all facets of farming from farm to fork through postharvest control of farms (Fig. 1.3).

Figure 1.3 Digital farming.

1.6 Artificial intelligence and machine learning

Artificial intelligence, known as AI, is the term used to describe systems or machines that can execute tasks while simulating human intelligence and can iteratively improve themselves depending on the data they gather. AI technologies help to increase productivity and efficiency. In every industry, AI solutions are helping to solve old problems. AI in agriculture is assisting farmers to increase their productivity and lessen adverse environmental effects. The agriculture sector has firmly and publicly embraced AI in its work to alter the result. With a 20% reduction in emissions from the agriculture sector, AI is changing how our food is produced. Utilizing AI technology is assisting in managing and controlling any unwelcome natural ailment. The field of computer science known as machine learning is used to create algorithms that can self-learn or learn on their own (Khairnar et al., 2022).

1.7 Applications of cyber-physical agricultural systems

Large-scale agricultural firms may easily monitor and control crop development, root growth, insect control, and other elements with the most recent ACPS technologies. The goal of the ACPS is to improve farming practices by fusing the electronic and biological realms. By modeling human and machine interaction after how a computer can adjust its behavior in reaction to environmental changes, CPSs describe the interaction between humans and machines. The agriculture system it will includes both virtual and physical systems as sensors, actuators, and communication tools (Khairnar et al., 2022). To boost crop yields while utilizing fewer labor and resources is one of modern agriculture’s top priorities. However, a few factors, including the measurement of real-world data, affect how valid this approach is. The platform known as Agrobio gives farmers new opportunities. The solution enables users to access information from experts, develop an online network of agricultural producers, and digitally share their data with the widest audience possible.

The ACPS can be used for many different purposes for all farmers in the world knowledge of the climate and weather is essential. Precision farming, smart farming, and effective agribusiness are some of the issues that ACPSs are intended to address to meet agricultural productivity concerns. These technologies are utilized for sophisticated data collecting and processing, remote monitoring and control, and resource management that is effective. Agribusiness cyber-systems can also be employed in manufacturing projects like quality assurance testing in the food sector.

1.8 More about agricultural cyber-physical system

Automate the gathering of data from soil/crop analyses, correlate it with various farming situations and production, and forecast soil performance for every given farm by using real-time data analytics, proactive analysis, and predictive models to identify and address issues (Vogl et al., 2016; Mirkouei, 2019).

Food security is greatly influenced by advanced farming. For instance, using remote devices (such as satellites and drones) for big data analytics enables better forecasting of crop yield estimation under a variety of conditions (Elmore et al., 2016). To determine sustainable, effective methods to achieve national priorities, agricultural infrastructure can be usefully understood through remote sensing and monitoring of terrestrial vegetation (FAO (Food and agriculture organization); United Nations., 2018).

They also included phenotyping studies for determining the pH level of the soil and the rate of soil nutrient depletion (Evenson & Gollin, 2003; Panke-Buisse et al., 2015). Later research concentrated on issues with productivity and sustainability, like crop analysis and optimization (Challinor et al., 2014; Tilman et al., 2002). Monitoring plant phenotypes is a part of agricultural analysis that helps to detect crop variety (Ray et al., 2012). Recent studies have created fundamental ideas for enhancing integrated technologies and assessments and promoting soil performance and the marketing of organic food through IoT (Hersh et al., 2019).

After mechanization, electrification, and automation, CPS is the fourth major industrial revolution. To locally control systems and analytics and optimize operational performance metrics (such as accuracy and reliability), CPS embraces physical components and advanced cyber-based technologies (e.g., high computation power, remote operations, and communication protocols) (Lee, 2015). To increase performance measurement in the smart manufacturing sector, advanced CPS, for instance, makes use of low-cost, small-size transistors that provide more flexibility of remote/in-line sensors (Monostori et al., 2016; Gao et al.,