scholarly journals Plant Factory: A New Playground of Industrial Communication and Computing

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 147
Author(s):  
Yu Liu ◽  
Sepehr Mousavi ◽  
Zhibo Pang ◽  
Zhongjun Ni ◽  
Magnus Karlsson ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Crop Production ◽  
Physical Systems ◽  
Vertical Farming ◽  
Plant Factory ◽  
Diverse Application ◽  
Speed Up ◽  
Solution Design ◽  
Application Requirements ◽  
Farming Industry

Plant Factory is a newly emerging industry aiming at transforming crop production to an unprecedented model by leveraging industrial automation and informatics. However, today’s plant factory and vertical farming industry are still in a primitive phase, and existing industrial cyber-physical systems are not optimal for a plant factory due to diverse application requirements on communication, computing and artificial intelligence. In this paper, we review use cases and requirements for future plant factories, and then dedicate an architecture that incorporates the communication and computing domains to plant factories with a preliminary proof-of-concept, which has been validated by both academic and industrial practices. We also call for a holistic co-design methodology that crosses the boundaries of communication, computing and artificial intelligence disciplines to guarantee the completeness of solution design and to speed up engineering implementation of plant factories and other industries sharing the same demands.

The main directions of scientific support for the implementation of the system of organic farming in Ukraine

2016 ◽  
Vol 1 (90) ◽  
pp. 22-24
Author(s):  
V.F. Kaminskyi ◽  
S.G. Korsun
Keyword(s):  
Land Use ◽  
Organic Farming ◽  
Crop Production ◽  
Land Ownership ◽  
Scientific Support ◽  
Speed Up ◽  
Organic Crop Production ◽  
Advanced Production ◽  
And Training ◽  
Organizational Issue

The aim of this work was to study the basic directions of scientific support introduction of organic farming in Ukraine. The study used methods of comparison, synthesis, analysis, induction and deduction. The article indicated on the main areas that need special attention from researchers and suggests one possible mechanism to remove the remaining obstacles to organizational issue introduction of scientific developments in the production of organic and training areas. This can speed up the creation of new and manage existing land ownership and land use organic farming with the introduction of advanced production technology of organic crop production.

scholarly journals Artificial Intelligence and the Internet of Things in Industry 4.0

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Razvan Nicolescu ◽  
Michael Huth ◽  
Omar Santos
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Leading Industry ◽  
Key Technologies ◽  
The Internet Of Things ◽  
State Of Art ◽  
Social Level

AbstractThis paper presents a new design for artificial intelligence in cyber-physical systems. We present a survey of principles, policies, design actions and key technologies for CPS, and discusses the state of art of the technology in a qualitative perspective. First, literature published between 2010 and 2021 is reviewed, and compared with the results of a qualitative empirical study that correlates world leading Industry 4.0 frameworks. Second, the study establishes the present and future techniques for increased automation in cyber-physical systems. We present the cybersecurity requirements as they are changing with the integration of artificial intelligence and internet of things in cyber-physical systems. The grounded theory methodology is applied for analysis and modelling the connections and interdependencies between edge components and automation in cyber-physical systems. In addition, the hierarchical cascading methodology is used in combination with the taxonomic classifications, to design a new integrated framework for future cyber-physical systems. The study looks at increased automation in cyber-physical systems from a technical and social level.

scholarly journals Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1807 ◽  
Author(s):  
Silke Hemming ◽  
Feije de Zwart ◽  
Anne Elings ◽  
Isabella Righini ◽  
Anna Petropoulou
Keyword(s):  
Artificial Intelligence ◽  
Crop Production ◽  
Vegetable Production ◽  
Digital Interface ◽  
Greenhouse Climate ◽  
Fresh Food ◽  
Net Profit ◽  
Greenhouse Vegetable ◽  
Process Computer ◽  
Cucumber Yield

The global population is increasing rapidly, together with the demand for healthy fresh food. The greenhouse industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) has reached breakthroughs in several areas, however, not yet in horticulture. An international competition on “autonomous greenhouses” aimed to combine horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams, consisting of scientists, professionals, and students with different backgrounds in horticulture and AI, participated in a greenhouse growing experiment. Each team had a 96 m2 modern greenhouse compartment to grow a cucumber crop remotely during a 4-month-period. Each compartment was equipped with standard actuators (heating, ventilation, screening, lighting, fogging, CO2 supply, water and nutrient supply). Control setpoints were remotely determined by teams using their own AI algorithms. Actuators were operated by a process computer. Different sensors continuously collected measurements. Setpoints and measurements were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a manually operated reference. Detailed results on cucumber yield, resource use, and net profit obtained by teams are explained in this paper. We can conclude that in general AI performed well in controlling a greenhouse. One team outperformed the manually-grown reference.

scholarly journals Challenges of digitalization and artificial intelligence for modern economies, societies and management

2020 ◽  
Vol 28 (3) ◽  
pp. 556-567
Author(s):  
Rolf Clauberg
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Technical Information ◽  
Developed Countries ◽  
Negative Effects ◽  
Physical Systems ◽  
Large Numbers ◽  
Official Policy ◽  
The Usa ◽  
New Jobs

This study aims at identifying the challenges of digitalization and artificial intelligence for modern economies, societies and business administration. The implementation of digitalization schemes as Industry 4.0 are presently official policy of many developed countries. The goal is optimization of production processes and supply chains. Artificial intelligence is also affecting many fields. Both technologies are expected to substantially change working conditions for many people. It is important to identify the kind and impact of these changes and possible means to minimize negative effects. For this purpose, this study uses previous results about the disappearance of manufacturing jobs in the USA and their impact on different groups of society together with technical information about the new technologies to deduce expected changes caused by digitalization and artificial intelligence. Results are that both technologies will destroy large numbers of jobs and complete job classes while at the same time creating new jobs very different from the ones destroyed. Extensive permanent education and re-education of employees will be necessary to minimize negative effects, probably even changes to a more broad-based education to improve the potential of job changes into completely new fields. In addition, the technical information about digitalization in cyber-physical systems points to dangers that will require solutions on the international level.

scholarly journals Performance Evaluation of Artificial Intelligence on Soil Property Detection

2018 ◽  
Vol 4 (10) ◽  
pp. 5
Author(s):  
Smriti Singhatiya ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Artificial Intelligence ◽  
Precision Agriculture ◽  
Crop Production ◽  
Nutrient Status ◽  
Soil Testing ◽  
Support Vector ◽  
Soil Test ◽  
New Era ◽  
Agriculture Soil ◽  
Historical Role

Now-a-days there is a need to study the nutrient status in lower horizons of the soil. Soil testing has played historical role in evaluating soil fertility maintenance and in sustainable agriculture. Soil testing shall also play its crucial role in precision agriculture. At present there is a need to develop basic inventory as per soil test basis and necessary information has to be built into the system for translating the results of soil test to achieve the crop production goal in new era. To achieve this goal artificial intelligence approach is used for predicting the soil properties.  In this paper for analysing these properties support vector regression (SVR), ensembled regression (ER) and neural network (NN) are used. The performance is evaluated with respect to MSE and RMSE and it is observed that ER outperforms better with respect to SVR and NN.

Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models for Cyber-Physical Systems

2021 ◽  
pp. 42-67
Author(s):  
Evren Daglarli
Keyword(s):  
Artificial Intelligence ◽  
Cyber Physical Systems ◽  
Black Box ◽  
The Internet ◽  
Learning Models ◽  
Physical Systems ◽  
Box Models ◽  
Explainable Artificial Intelligence ◽  
Meta Learning ◽  
Black Box Models

Today, the effects of promising technologies such as explainable artificial intelligence (xAI) and meta-learning (ML) on the internet of things (IoT) and the cyber-physical systems (CPS), which are important components of Industry 4.0, are increasingly intensified. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. For these reasons, it is necessary to make serious efforts on the explanability and interpretability of black box models. In the near future, the integration of explainable artificial intelligence and meta-learning approaches to cyber-physical systems will have effects on a high level of virtualization and simulation infrastructure, real-time supply chain, cyber factories with smart machines communicating over the internet, maximizing production efficiency, analysis of service quality and competition level.

Artificial intelligence for securing industrial-based cyber–physical systems

2021 ◽  
Vol 117 ◽  
pp. 291-298
Author(s):  
Zhihan Lv ◽  
Dongliang Chen ◽  
Ranran Lou ◽  
Ammar Alazab
Keyword(s):  
Artificial Intelligence ◽  
Cyber Physical Systems ◽  
Physical Systems
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