Agro 4.0: A data science-based information system for sustainable agroecosystem management

Fonseca, Eugênio Pacceli Reis da; Caldeira, Evandro; Filho, Heitor Soares Ramos; Oliveira, Leonardo B.; Pereira, Adriano C. M.; Vilela, Pierre Santos

doi:10.1016/j.simpat.2020.102068

Cited by 10 publications

(11 citation statements)

References 42 publications

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“…To analyze or assess sustainability is to find the dynamic, complex, and composite data relationship among human activities and natural ecosystems [36]. Since human-nature interactions happen in the geographic space, the data indicators in sustainability research are georeferenced [37]. The geographical properties of these indicators, such as regionalities and locations, are essential dimensions for sustainability research [38,39].…”

Section: Related Work 21 Sustainability In Geospatial Databasesmentioning

confidence: 99%

“…In recent years, data science has driven increased attention as a promising solution in sustainability analysis. Many studies organize such multi-scale and multidimensional relevant data in a geospatial or georeferenced database, for fast retrieval, efficient storage, flexible analysis, and data mining [36][37][38]. Geospatial databases can collect, store, and index large-scale georeferenced data for long-term analysis.…”

Section: Related Work 21 Sustainability In Geospatial Databasesmentioning

confidence: 99%

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Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

Sun

Jin

2023

Sustainability

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Geospatial databases are basic tools to collect, index, and manage georeferenced data indicators in sustainability research for efficient, long-term analysis. NoSQL databases are increasingly applied to manage the ever-growing massive spatial vector data (SVD) with their changeable data schemas, agile scalability, and fast query response time. Spatial queries are basic operations in geospatial databases. According to Green information technology, an efficient spatial index can accelerate query processing and save power consumption for ubiquitous spatial applications. Current solutions tend to pursue it by indexing spatial objects with space-filling curves or geohash on NoSQL databases. As for the performance-wise R-tree family, they are mainly used in slow disk-based spatial access methods on NoSQL databases that incur high loading and searching costs. Therefore, performing spatial queries efficiently with the R-tree family on NoSQL databases remains a challenge. In this paper, an in-memory balanced and distributed R*-tree index named the BDRST index is proposed and implemented on HBase for efficient spatial-query processing of massive SVD. The BDRST index stores and distributes serialized R*-trees to HBase regions in association with SVD partitions in the same table. Moreover, an efficient optimized server-side parallel processing framework is presented for real-time R*-tree instantiation and query processing. Through extensive experiments on real-world land-use data sets, the performance of our method is tested, including index building, index quality, spatial queries, and applications. Our proposed method outperforms other state-of-the-art solutions, saving between 27.36% and 95.94% on average execution time for the above operations. Experimental results show the capability of the BDRST index to support spatial queries over large-scale SVD, and our method provides a solution for efficient sustainability research that involves massive georeferenced data.

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Section: Related Work 21 Sustainability In Geospatial Databasesmentioning

confidence: 99%

Section: Related Work 21 Sustainability In Geospatial Databasesmentioning

confidence: 99%

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

Sun

Jin

2023

Sustainability

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“…The concluded that the biggest numbers of applications was dominantly developed for monitoring of agricultural processes using some kind of visualization techniques and only small number of studies was dedicated to development of applications that had some possibilities for prediction of future behaviors which could lead to better decision making processes. In [21], an IoT and data science-based information system is described. Developed system provided tools for data collection, processing, visualization, simulation and analysis.…”

Section: Figure 8 Gogrow Data Analysismentioning

confidence: 99%

The role and importance of Smart Systems concept in the Industry 4.0

Amovic

Vujović

Milinković

2020

IJEEC

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From the 18th century, the Industrial Revolutions led to the creation of a base for the development and progress of civilization and business. Different business models that relied on the new trends defined by the industrial revolutions significantly transformed the ecosystems of companies. This resulted in the accelerated development and entrance into the digital age, followed by the digitization of the environment and establishing a new system of values. Information and communication technologies are the backbone of the development and mapping of the physical domain into cyber-physical, leading to the emergence of new processes, knowledge, and skills that have been established in the context of Digital Transformation and the digital era. The key part of Digital transformation process is the Smart Systems concept, which is present in almost all industries and domains. Smart Agriculture is one of the most common applications of Smart Systems solutions. It aims to answer important questions regarding food production while, at the same time, taking care of the preservation of the environment, the health of the population and global development. In this paper, the key concepts of the digital transformation and smart systems are presented. In addition, the design and development of the GoGrow, commercial solution for smart agriculture is described.

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“…A data science-based information system for agroecosystem sustainability management that enables to using the ISA methodology, gather, organize, and validate data on agroecosystem sustainability. The first simulation involves doing feature selection experiments on various cuts of the dataset to replicate the calculation of the ISA sustainability index with a less amount of data for each property [3]. The PVA/SCN ionic polymer fabrication method and silver electrode layers were pasted on both surfaces of the PVA/SCN ionic polymer to develop sensors with a paintbrush and measured dielectric data with the electric field and sensing electrical signals with bending strain.…”

Section: Introductionmentioning

confidence: 99%

Deployment of e-services based contextual smart agro system using internet of things

et al. 2022

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Climate change's effects are becoming more apparent, and farmers are bearing the brunt of the consequences. As a result, by 2050, food production is expected to decline by 18%. Therefore, the study's goal is to develop effective and well-organized roadmap for context-based smart agricultural systems using a pre-determined ICT framework. Following that, this study offers a four-level conceptual framework for an e-services-based smart agro system utilizing internet of things (IoT). Here, each level optimizes the IoT infrastructure to accept e-services based on contextual information supplied by the e-services. Furthermore, the proposed ICTization process intends to broaden the role of ICT technology development. Besides, the system's views decrease misunderstandings about technology, growth, and connectivity while also enhancing raw data, administration, and service synchronization. Farmers, agricultural officers, and network operators, for example, are all included in the proposed roadmap, which includes omnipresent farm treatment services. Precision farming, on the other hand, need new knowledge and innovation in order to achieve an integrated and comprehensive approach to technology.

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Agro 4.0: A data science-based information system for sustainable agroecosystem management

Cited by 10 publications

References 42 publications

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

The role and importance of Smart Systems concept in the Industry 4.0

Deployment of e-services based contextual smart agro system using internet of things

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