Use of IoT technologies for irrigation and plant protection

Moysiadis, Theocharis; Adamides, George; Stylianou, Andreas; Ζώτος, Νικόλαος; Giannakopoulou, Marianthi; Alexiou, George

doi:10.1016/b978-0-12-819774-5.00010-2

Cited by 9 publications

(9 citation statements)

References 21 publications

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“…These interconnect numerous sensors online while their power lies on data exhaustion and reusability by various services or applications. That said, European farmers should be Smart Farming early adopters since they belong in the region with the world's largest lands devoted to agriculture (Moysiadis et al. , 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Smart Farming context has substantial influence on the entire production systems (O'Grady and O'Hare, 2017;Pham and Stack, 2018) and ties closely with the AgriFood supply chain, transportation and logistics (Vlachos et al, 2008;Zissis et al, 2017) and food-waste (Despoudi et al, 2018) and therefore is reshaping the whole sector following the same way as most digital transformations have gone through (Li et al, 2016). Data are used to provide visibility of the processes from farm-to-fork (Wolfert et al, 2014), as well as predictive insights in farming operations, drive real-time operational decisions, and redesign business processes for game-changing business models (Nukala et al, 2016;Wolfert et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Information sharing practices of the AgriFood SC emphasize recently on the use of data and technology in the cyber-physical farm management cycle, with a strong focus on dataintensive, informed decisions for the agricultural practices (Kaloxylos et al, 2012;Nukala et al, 2016). The agrarian data, linked data, metadata, information, and knowledge could be vast and include anything associated with them.…”

Section: Introductionmentioning

confidence: 99%

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AgriFood supply chain traceability: data sharing in a farm-to-fork case

Moysiadis¹,

Spanaki

Kläser

et al. 2022

BIJ

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PurposeTraceability of food is of paramount importance to the increasingly sustainability-conscious consumers. Several tracking and tracing systems have been developed in the AgriFood sector in order to prove to the consumers the origins and processing of food products. Critical challenges in realizing food's traceability include cooperating with multiple actors on common data sharing standards and data models.Design/methodology/approachThis research applies a design science approach to showcase traceability that includes preharvest activities and conditions in a case study. The authors demonstrate how existing data sharing standards can be applied in combination with new data models suitable for capturing transparency information about plant production.FindingsTogether with existing studies on farm-to-fork transparency, our results demonstrate how to realize transparency from field to fork and enable producers to show a complete bill of sustainability.Originality/valueThe existing standards and data models address transparency challenges in AgriFood chains from the moment of harvest up to retail (farm-to-fork) relatively well, but not what happens before harvest. In order to address sustainability concerns, there is a need to collect data about production activities related to product quality and sustainability before harvesting and share it downstream the supply chain. The ability to gather data on sustainability practices such as reducing pesticide, herbicide, fertilizer and water use are crucial requirements for producers to market their produce as quality and sustainable products.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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AgriFood supply chain traceability: data sharing in a farm-to-fork case

Moysiadis¹,

Spanaki

Kläser

et al. 2022

BIJ

Self Cite

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“…Besides, in real scenarios, the data available from a unique type of sensor might not be sufficient to fully represent the FSC problem that is intended to be addressed. For instance, different Internet of Things (IoT) devices (e.g., agricultural environment monitoring systems, GPS, cameras) provide diverse data for the optimum management of production systems [ 147 , 148 , 149 ]. Amongst the relevant data for the aforementioned example, we find temperature, humidity, localization coordinates, crop images, and others.…”

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

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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.

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“…Such applications are particularly important in the vegetable supply chain, especially at the agricultural stage, where accurate control of indicators is often key to enhancing crop productivity. For instance, operational parameters such as pesticide and water use have been optimised by using IoT systems ( Moysiadis et al, 2021 ). Other parameters that have been controlled via IoT include soil nutritional content, humidity, temperature, and plant physiology, such as the vegetative index, nutritional requirements, electrical conductivity, and magnetic susceptibility ( Maraveas and Bartzanas, 2021 ).…”

Section: Interconnection Between Vegetal and Digital Trendsmentioning

confidence: 99%