Increasing Profitability and Monitoring Environmental Performance: A Case Study in the Agri-Food Industry through an Edge-IoT Platform

Pérez-Pons, María E.; Plaza-Hernández, Marta; Alonso, Ricardo S.; Parra-Domínguez, Javier; Prieto, Javier

doi:10.3390/su13010283

Cited by 20 publications

(12 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nowadays, CI approaches are usually focused on maximizing accuracy without considering the resources they consume. Nevertheless, with the increasing demand of developing a globally low carbon economy, the need to build an intelligent and environmentally friendly food industry is becoming more urgent [ 174 , 175 ]. In this sense, CI approaches require that a balance be struck between the computational resources they use, the energy they consume, and the performance they can achieve in the time-frame allowed by food manufacturing processes [ 176 ].…”

Section: Discussionmentioning

confidence: 99%

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Angarita-Zapata

Alonso-Vicario

Masegosa

et al. 2021

Sensors

View full text Add to dashboard Cite

In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a data-driven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC.

show abstract

Section: Discussionmentioning

confidence: 99%

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Angarita-Zapata

Alonso-Vicario

Masegosa

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The amount that is purchased in the harvest season, as well as the amount that is recovered in each selling period, have a strong impact on a wholesaler’s profit [ 78 ]. The proposed MAS allow to buy agricultural products sustainably thanks to the use of technologies such as edge computing, which reduces agricultural costs and help to make efficient use of resources [ 16 ] such as water [ 20 , 79 ] or energy optimization [ 21 ], as well as monitoring harmful emissions. According to the work in [ 78 ], an optimal selling policy can increase the expected profit.…”

Section: Discussionmentioning

confidence: 99%

“…This is commonplace in scenarios where Internet connectivity is limited (for example, rural agricultural environments) [ 15 ]. The EC paradigm has been also used in different studies in which results show that including the costs of edge and non-edge data transfer has an impact on the efficiency [ 16 ].…”

Section: Related Workmentioning

confidence: 99%

“…In this regard, the Internet of Things (IoT) and, more specifically, the Industrial Internet of Things (IIoT), is presented as a key enabling technology for implementing and monitoring resource management solutions in various scenarios in Industry 4.0, including smart agriculture environments [ 11 ]. The monitoring of all these values can be carried out with sensors, using new paradigms such as edge computing which enable monitoring while reducing the cost of data analysis in the cloud [ 14 , 15 ] increase the efficiency of agricultural processes [ 16 ]. The application of multi-agent systems to monitor agricultural processes is common, especially to achieve the efficient use of land in terms of investment and production [ 17 ], to manage resources [ 18 ], to increase the efficiency of irrigation systems [ 19 , 20 ], to optimize energy use [ 21 ], or predict the prices of agricultural products [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market

Pérez-Pons

Alonso

García

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Yearly population growth will lead to a significant increase in agricultural production in the coming years. Twenty-first century agricultural producers will be facing the challenge of achieving food security and efficiency. This must be achieved while ensuring sustainable agricultural systems and overcoming the problems posed by climate change, depletion of water resources, and the potential for increased erosion and loss of productivity due to extreme weather conditions. Those environmental consequences will directly affect the price setting process. In view of the price oscillations and the lack of transparent information for buyers, a multi-agent system (MAS) is presented in this article. It supports the making of decisions in the purchase of sustainable agricultural products. The proposed MAS consists of a system that supports decision-making when choosing a supplier on the basis of certain preference-based parameters aimed at measuring the sustainability of a supplier and a deep Q-learning agent for agricultural future market price forecast. Therefore, different agri-environmental indicators (AEIs) have been considered, as well as the use of edge computing technologies to reduce costs of data transfer to the cloud. The presented MAS combines price setting optimizations and user preferences in regards to accessing, filtering, and integrating information. The agents filter and fuse information relevant to a user according to supplier attributes and a dynamic environment. The results presented in this paper allow a user to choose the supplier that best suits their preferences as well as to gain insight on agricultural future markets price oscillations through a deep Q-learning agent.

show abstract

“…Globalization has radically informed the development of sustaining and disruptive technologies (Schuelke-Leech, 2018), where traditional industries such as agriculture and aquaculture employ vanguard technologies to expand upon opportunities that has enabled smart farming and the agri-food industry 4.0 (Klerkz et al, 2019). Perez-Pons et al (2021) highlighted pressing need to make farms more profitable and sustainable via the analysis of data envelopment analysis and the application of the Internet of things and Edge computing; this approach allows monitoring environmental conditions with real-time data from the different sensors installed on the farm; thus, minimizing costs and achieving robustness by way of transitioning important data to the cloud after edge computing. Essentially, the edge devices process the data and then decide either to send this data to the cloud for further processing or make decision locally at the edge (Table 1).…”

Section: Edge-internet Of Things (Iot) Systemsmentioning

confidence: 99%

Digital transformation of peatland eco-innovations (‘Paludiculture’): Enabling a paradigm shift towards the real-time sustainable production of ‘green-friendly’ products and services

Rowan

Murray

Qiao

et al. 2022

Science of The Total Environment

View full text Add to dashboard Cite

Increasing Profitability and Monitoring Environmental Performance: A Case Study in the Agri-Food Industry through an Edge-IoT Platform

Cited by 20 publications

References 66 publications

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market

Digital transformation of peatland eco-innovations (‘Paludiculture’): Enabling a paradigm shift towards the real-time sustainable production of ‘green-friendly’ products and services

Contact Info

Product

Resources

About