Conceptual design of smart farming solution for precise agriculture

Budaev, D.; Lada, A.; Simonova, E.; Skobelev, P.; Travin, V.; Yalovenko, O.; Voshchuk, G.; Zhilyaev, A.

doi:10.2495/dne-v13-n3-309-316

Cited by 9 publications

(7 citation statements)

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“…Although there is a suggestion that skilled agricultural workers have the highest probability of automation compared to other workers (Nedelkoska and Quintini, 2018), the extent to which this will support or replace decisions in farming depends on the technology. Sensors provide raw data (e.g., weather data), and smart devices (robotic vehicles, drone mounted cameras) will allow sophisticated farm management advice (Walter et al, 2017), while smart systems have the capability to execute autonomous actions (Budaev et al, 2019). For the former, human interpretive skills for decision making are still important, but for the latter the role of humans in analysis and planning is increasingly assisted by machines.…”

Section: Disruptions To Farmer Knowledge and Decision-makingmentioning

confidence: 99%

“…The extent to which digital agriculture will disrupt or enable these network processes is an important consideration. Proposed smart systems, which promise to take and learn the best practices from advanced precise farmers, formalize and transfer their knowledge and support to other farmers in everyday decision making (Budaev et al, 2019) could arguably replace interpersonal networks. However, the potential for digital technologies to support collaborative knowledge creation has also been identified (Eastwood et al, 2012).…”

Section: Enabling or Disrupting Farmers' Knowledge Networkmentioning

confidence: 99%

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What Are the Implications of Digitalisation for Agricultural Knowledge?

Ingram

Maye

2020

Front. Sustain. Food Syst.

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In this perspective paper we consider the implications of a digital transformation for agricultural knowledge, a subject which hitherto has received limited attention. We raise critical questions about how digital agriculture will intersect with established modes of knowing and decision-making. We also consider the implications for the wider Agricultural Knowledge and Innovation System (AKIS), specifically the roles and capabilities of those who provide advice to farmers, as well as those responsible for data analytics, and the organizations and institutions that link and support them. We conclude that new data driven processes on farm, as well as the changing AKIS dynamic under digital agriculture, bring new demands, relations and tensions to agricultural decision-making, but also create opportunities to foster new learning by harnessing synergies in the AKIS.

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Section: Disruptions To Farmer Knowledge and Decision-makingmentioning

confidence: 99%

Section: Enabling or Disrupting Farmers' Knowledge Networkmentioning

confidence: 99%

What Are the Implications of Digitalisation for Agricultural Knowledge?

Ingram

Maye

2020

Front. Sustain. Food Syst.

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“…Once again, a certain imbalance has occurred in technology development. The huge growth and development of engineering has not resolved the organizational and technological issues that arise in the eld of mobile energy [8]. The solution of the problem posed may lie in the ways of justifying and developing mobile energy resources in the fth generation of machines-MPUs, not tractors-which have greater capabilities than the traditional tractors and are more sophisticated at the core components.…”

Section: Methodsmentioning

confidence: 99%

Agricultural tractors of the fifth generation

Lipkovich

Nesmiyan

Nikitchenko³

et al. 2018

Scientia Iranica

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In this study, the problem of substantiation of Mobile Power Units (MPUs)tractors for agriculture of the fth generation{is described. The aim is to analyze agricultural tractors of the fth generation. Technogenic interaction of MPUs with production processes in agro-ecosystems of grain production in the zonal conditions of arid agriculture in Southern Russia is described. Proposals for new MPUs are developed and the technological structures of machine aggregates based on the MPUs of the fth generation of classes 3, 5{6 and 8, to determine their technical schemes, are determined. Analytical models are provided for calculating the power characteristics of MPUs of the mentioned classes in deterministic and stochastic modes in relation to the most energyintensive operations with de nitions of the mathematical expectations of Machine-Tractor Aggregate (MTA) power and performance. E ectiveness of MTAs is analyzed for the new MPUs based upon on the criterion of the time spent on cultivating crop rotations for classes 2 and 5{6 in wheeled and caterpillar performances and they are applied to the new generation of Machine-Tractor Station (MTS) of the technological services type. A comparison is carried out for the same sites with MTAs using the fourth-generation tractors. It will be observed that the time costs are reduced by 1.4{1.5 times and for the MPU of class 8, the reduction is up to 2.3 times.

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“…Furthermore, the visualisation of the ontology shows that Machine A needs a connection to the Waste Water Pipe and is powered through a Power Supply Connection. Every class carries certain attributes [30] which again are connected to the class in form of Data Properties: has Position, has Type or has Port.…”

Section: Using Ontologies For Automatic Design Validationmentioning

confidence: 99%

How to Improve Collaboration Efficiency in the Built Environment of Factories by Using an Integrated Factory Modelling Concept – An Expert Study

Burggräf¹,

Dannapfel²,

Esfahani³

et al. 2020

IJDNE

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Factory design involves the design of a complex production system in coherence with the requirements of the surrounding building envelope. These two disciplines, however, are still not integrated in order to be able to perform a completely BIM-based factory design that can be used to minimise design-related delays and reduce the cost of a factory design project. Main obstacles are unaligned levels of development, lack of transparency of informational relations and inefficient collaboration between production design and MEP(H) design. The objective of this paper is hence the generation and discussion of two concrete solution recommendations through an explorative expert study to overcome the already identified obstacles hindering an efficient BIM-based factory design process.

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Conceptual design of smart farming solution for precise agriculture

Cited by 9 publications

References 0 publications

What Are the Implications of Digitalisation for Agricultural Knowledge?

What Are the Implications of Digitalisation for Agricultural Knowledge?

Agricultural tractors of the fifth generation

How to Improve Collaboration Efficiency in the Built Environment of Factories by Using an Integrated Factory Modelling Concept – An Expert Study

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