Tools and framework for cyber-physical agricultural systems
subject: Agricultural sensors; artificial intelligence (AI); blockchain; cloud computing; crop modeling; cyber-physical systems (CPS); decision support systems; digital twins; drones; edge computing; environmental monitoring; farm management information systems (FMIS); food safety; Internet of Things (IoT); weather forecasting; wireless sensor networks (WSN)
description: The development of cyber-physical agricultural systems (CPASs) has created new opportunities for precision farming, sustainable food production, and efficient use of resources. CPAS leverages advanced technologies such as the Internet of Things, artificial intelligence (AI), and machine learning (ML) to collect, analyze, and utilize data to improve farming practices. However, the implementation of CPAS requires the use of various tools and frameworks to ensure seamless integration and communication between different components of the system. One of the key tools for CPAS is sensors. This chapter focuses on key tools for CPAS, such as sensors that can collect data on environmental factors, including temperature, humidity, soil moisture, and nutrient levels, enabling farmers to monitor crop growth and identify issues. The use of drones equipped with cameras and sensors can provide a birds eye view of farmland, allowing farmers to detect issues that are difficult to detect otherwise. Frameworks such as the Open Platform Communication Unified Architecture (OPC-UA) provide a standardized approach to communication between different devices and systems in agricultural systems. OPC-UA enables secure and efficient data exchange between sensors, machines, and other components, enabling the integration of various tools and frameworks within CPAS. This framework ensures that different components of CPAS can communicate seamlessly, leading to more efficient and effective farming practices. Another critical framework for CPAS is the decision support system (DSS). DSS utilizes AI and ML algorithms to analyze data from various sources and provide recommendations to farmers. For example, DSS can provide guidance on crop selection, planting dates, irrigation schedules, and pest management. This framework can assist farmers in making informed decisions that can increase yield, reduce waste, and improve sustainability. 2024 Elsevier Inc. All rights reserved.
creator: Mohiddin S.K.; Sharmila S.; Sharma V.
source: Agri 4.0 and the Future of Cyber-Physical Agricultural Systems, pp. 37-53.
publisher: Elsevier
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 978-044313185-1; 978-044313186-8
format: Online
language: English
type: Book chapter
coverage: Mohiddin S.K., Department of Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Vaddeswaram, Guntur, India; Sharmila S., Department of IT, VNITSW, Andhra Pradesh, Guntur, India; Sharma V., Department of Computational Sciences, Christ University, Delhi NCR Campus, India