mySense: A comprehensive data management environment to improve precision agriculture practices

Morais, Raul; Silva, Nuno; Mendes, Jorge; Adão, Telmo; Pádua, Luís; López-Riquelme, Juan Antonio; Pavón-Pulido, Nieves; Sousa, Joaquim J.; Peres, Emanuel

doi:10.1016/j.compag.2019.05.028

Cited by 86 publications

(39 citation statements)

References 26 publications

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“…Within the 5G system, an internet of things (IoT) can be established with low latency, enabling real time soil measurement and response. For instance, unmanned aerial vehicle (UAV) based remote sensing can be coupled with soil amendment delivery in precision agricultural practice (Kota and Giambene, 2019;Morais et al, 2019). Big data applications with machine learning also provide predictive power, facilitating smart farming to save energy, water, and cost, while increasing crop yields (Wolfert et al, 2017).…”

Section: Knowledge Transfermentioning

confidence: 99%

Sustainable soil use and management: An interdisciplinary and systematic approach

Hou

Bolan

Tsang

et al. 2020

Science of The Total Environment

214

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Soil degradation impedes achieving the United Nations' Sustainable Development Goals. • Soil plays a fundamental role for biodiversity conservation. • Soil researchers ought to prioritize the multifunctional value of soil health. • A framework for interdisciplinary research in soil sustainability is presented. • Information management and knowledge sharing may drive sustainable behavior change.

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Section: Knowledge Transfermentioning

confidence: 99%

Sustainable soil use and management: An interdisciplinary and systematic approach

Hou

Bolan

Tsang

et al. 2020

Science of The Total Environment

214

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“…A new proposal was presented for agriculture applications by investigating various integrated platforms, which include cloud computing, IoT, and data mining techniques [26]. A scalable network-based architecture was proposed to monitor and regulate agriculture farms in rural areas with the help of IoT-based WiFi with long-distance network and fog computing [27].…”

Section: Related Workmentioning

confidence: 99%

Sensors Driven AI-Based Agriculture Recommendation Model for Assessing Land Suitability

Vincent

Deepa

Elavarasan

et al. 2019

Sensors

160

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The world population is expected to grow by another two billion in 2050, according to the survey taken by the Food and Agriculture Organization, while the arable area is likely to grow only by 5%. Therefore, smart and efficient farming techniques are necessary to improve agriculture productivity. Agriculture land suitability assessment is one of the essential tools for agriculture development. Several new technologies and innovations are being implemented in agriculture as an alternative to collect and process farm information. The rapid development of wireless sensor networks has triggered the design of low-cost and small sensor devices with the Internet of Things (IoT) empowered as a feasible tool for automating and decision-making in the domain of agriculture. This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability. This proposed system will help the farmers to assess the agriculture land for cultivation in terms of four decision classes, namely more suitable, suitable, moderately suitable, and unsuitable. This assessment is determined based on the input collected from the various sensor devices, which are used for training the system. The results obtained using MLP with four hidden layers is found to be effective for the multiclass classification system when compared to the other existing model. This trained model will be used for evaluating future assessments and classifying the land after every cultivation.

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“…The race to achieve a universal and generic system of IoT solutions in order to fulfil all these requirements has become one of the pressing scientific concerns of the last few years [10][11][12]. A software ecosystem, traditionally called as middleware (Bandyopadhyay et al [10]), now baptised as an IoT platform due to its decentralised and mixture pieces, is used to meet all these requirements [13,14].…”

Section: Introductionmentioning

confidence: 99%

An IoT Platform Based on Microservices and Serverless Paradigms for Smart Farming Purposes

Trilles

González‐Pérez

Huerta

2020

Sensors

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Nowadays, the concept of “Everything is connected to Everything” has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety of scenarios creates different challenges such as the heterogeneity of IoT devices, support for large numbers of connected devices, reliable and safe systems, energy efficiency and the possibility of using this system by third-parties in other scenarios. A transversal middleware in all IoT solutions is called an IoT platform. the IoT platform is a piece of software that works like a kind of “glue” to combine platforms and orchestrate capabilities that connect devices, users and applications/services in a “cyber-physical” world. In this way, the IoT platform can help solve the challenges listed above. This paper proposes an IoT agnostic architecture, highlighting the role of the IoT platform, within a broader ecosystem of interconnected tools, aiming at increasing scalability, stability, interoperability and reusability. For that purpose, different paradigms of computing will be used, such as microservices architecture and serverless computing. Additionally, a technological proposal of the architecture, called SEnviro Connect, is presented. This proposal is validated in the IoT scenario of smart farming, where five IoT devices (SEnviro nodes) have been deployed to improve wine production. A comprehensive performance evaluation is carried out to guarantee a scalable and stable platform.

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mySense: A comprehensive data management environment to improve precision agriculture practices

Cited by 86 publications

References 26 publications

Sustainable soil use and management: An interdisciplinary and systematic approach

Sustainable soil use and management: An interdisciplinary and systematic approach

Sensors Driven AI-Based Agriculture Recommendation Model for Assessing Land Suitability

An IoT Platform Based on Microservices and Serverless Paradigms for Smart Farming Purposes

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