Development of a Knowledge Base in the “Smart Farming” System for Agricultural Enterprise Management

Skobelev, Petr; Simonova, Elena; Smirnov, Sergey V.; Budaev, Denis; Voshchuk, G. Yu.; Morokov, A.L.

doi:10.1016/j.procs.2019.02.029

Cited by 47 publications

(22 citation statements)

References 7 publications

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“…Agriculture 4.0 is known with several names including data-driven and automated agriculture [172,257], intelligence agriculture [258], smart agriculture [259], digital agriculture [260], digital farming [261], smart farming [262], and farming 4.0 [83]. Agriculture 4.0 emerged when telematics and data management were combined to the already known concept of precision agriculture (improving the accuracy of operations) [172].…”

Section: E Drivers Of Energy 40mentioning

confidence: 99%

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Bongomin

Yemane

Kembabazi

et al. 2020

Journal of Engineering

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Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors.

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Section: E Drivers Of Energy 40mentioning

confidence: 99%

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Bongomin

Yemane

Kembabazi

et al. 2020

Journal of Engineering

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“…They assessed the performance of the algorithm with accuracy of 99% for land suitability [39]. Authors in [40] developed knowledge base and multi-agent system for management of agricultural enterprises. Pachayappan et al in [41] developed an IoT based applications for different sectors like oil and refinery, pharma, manufacturing etc.…”

Section: Fig1 the Missing Links In National Collaboration In Agriculmentioning

confidence: 99%

Systems Science: Digital Transformation andDeveloping Business Model toward Smart Farms’platform

Ibrahim

Elramly

Hassan

2021

International Journal of Circuits, Systems and Signal Processing

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This paper describes efforts by National Authority for Remote Sensing and Space Sciences (NARSS) to help the Egyptian government to manage and monitor the national projects. We successfully developed a geospatial data sharing portal (NARSSGeoPortal) as part of the government need to build national Decision Support System (DSS). We were able to solve the software development issues as well as the satellite imagery sourcing issues, but the main challenge remains around how to collect complete and correct data from the public about their private businesses nationwide. The most challenging is how to engage the public and encourage the business owners who are the main sources of data to provide the government Geoportal with data about their businesses. It is also challenging to engage the scientists and experts from government research centers into the data sharing Geoportal. Furthermore, it is a challenge to integrate the government research centers with the public businesses’ daily operation. The data sharing Geoportal is built for all national projects and government authorities, however, in this paper we focus on the Agriculture authorities and farming businesses where the challenge is how to collect correct and complete data per acre about the seeds, fertilizers, water, pest control and all other farm related data that the satellite imagery does not provide. The goal is to integrate the farms into unified national monitoring, and control system while developing advanced smart farms with the use of Internet of Things (IoT). The proposed collaboration agriculture platform fills the gap between two groups. The first group includes the government authorities, financial institutions, and research centers. The second group includes farmers, supply chain, and agriculture engineers. The platform show how employment can be generated by transforming the national ecosystem. The paper also fills a major gap in industry as well as in academia by providing the first Bluetooth Low Energy computer aided design tool that will facilitate testing, designing, deploying, managing and debugging of real Bluetooth Low Energy networks.

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“…The proposed methodology was built into a system that could recognize crop pests with the help of the knowledge base. A system called "Smart Farming" was proposed in [3] for precision farming management, where a knowledge base was developed. Information was organized in the knowledge base in the form of a semantic network consisting of concepts and relations.…”

Section: Knowledge-based and Ontology-based Approachesmentioning

confidence: 99%

Development of Knowledge Base Using Human Experience Semantic Network for Instructive Texts

2021

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An organized knowledge structure or knowledge base plays a vital role in retaining knowledge where data are processed and organized so that machines can understand. Instructive text (iText) consists of a set of instructions to accomplish a task or operation. Hence, iText includes a group of texts having a title or name of the task or operation and step-by-step instructions on how to accomplish the task. In the case of iText, storing only entities and their relationships with other entities does not always provide a solution for capturing knowledge from iTexts as it consists of parameters and attributes of different entities and their action based on different operations or procedures and the values differ for every individual operation or procedure for the same entity. There is a research gap in iTexts that created limitations to learn about different operations, capture human experience and dynamically update knowledge for every individual operation or instruction. This research presents a knowledge base for capturing and retaining knowledge from iTexts existing in operational documents. From each iTexts, small pieces of knowledge are extracted and represented as nodes linked to one another in the form of a knowledge network called the human experience semantic network (HESN). HESN is the crucial component of our proposed knowledge base. The knowledge base also consists of domain knowledge having different classified terms and key phrases of the specific domain.

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Development of a Knowledge Base in the “Smart Farming” System for Agricultural Enterprise Management

Cited by 47 publications

References 7 publications

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Systems Science: Digital Transformation andDeveloping Business Model toward Smart Farms’platform

Development of Knowledge Base Using Human Experience Semantic Network for Instructive Texts

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