The State and Future of Smart Agriculture: Insights from mining social media

Ofori, Martinson; El‐Gayar, Omar F.

doi:10.1109/bigdata47090.2019.9006587

Cited by 7 publications

(7 citation statements)

References 31 publications

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“…The parameters f and g and their effect on the evaluation and decision results are analyzed. As shown in Table 6, when f = 1, g randomly takes the value of [3][4][5][6][7][8][9][10][11][12][13][14][15], and the four solutions are ranked as Longsoft > CCMP > Chinasoft > XAG; when g = 1 and f = 3, the ranking changes to Chinasoft > JXIOT > Longsoft > XAG, and the optimal solution changes from Longsoft to Chinasoft; then, when f randomly takes the values in [9][10][11][12][13][14][15], the ranking becomes Longsoft > JXIOT > Chinasoft > XAG. In addition, when randomly taking any combination of f and g, such as f = 5, g = 10, or f = 8, g = 5, the corresponding sorting becomes JXIOT > Chinasoft > Longsoft > XAG, and the best alternative becomes JXIOT.…”

Section: Analysis Of Examplesmentioning

confidence: 99%

“…To further analyze the effect of parameters f and g on sorting results, values of f and g are taken from [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], and the trend of score values for the corresponding four smart agricultural solutions is shown in Figures 6-9. Additionally, according to Figures 6-9, the score values of the four smart agricultural solutions increase with f and g. Because f and g represent the strength of the relationship, the larger their values are, the higher the expectation of the evaluator for the relationship between the indexes.…”

Section: Analysis Of Examplesmentioning

confidence: 99%

“…In the era of intelligent information, with the extensive application of the Internet of Things, big data, and intelligent technology in the field of agriculture, smart agriculture has gradually been formed 1,2 . As a new model of agricultural development, smart agriculture refers to the intelligent development and management of agriculture by fully gathering modern technologies, such as mobile terminals, IT platforms, cloud computing, big data, the Internet of Things, 3S technologies, and the wisdom and knowledge of experts, 3,4 as shown in Figure 1. Smart agriculture, primarily used in precision agriculture, intelligent irrigation, agricultural monitoring, agricultural management systems, traceability of agricultural product quality and safety, 5 is conducive to promoting fine and efficient agricultural production and ultimately to promoting the sustainable development of agriculture.…”

Section: Introductionmentioning

confidence: 99%

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Assessment and selection of smart agriculture solutions using an information error‐based Pythagorean fuzzy cloud algorithm

Yang

Lin

et al. 2021

Int J of Intelligent Sys

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Smart agriculture can enhance agricultural production efficiency, improve the ecological environment, and realize the sustainable development of agriculture. Many countries and companies are working hard to develop or introduce smart agricultural solutions. Because of the shackles of traditional agricultural management methods and fierce competition with a variety of different solutions, it is a difficult task for enterprises to select and implement smart agricultural solutions smoothly. Hence, enterprises must assess alternative solutions and select a feasible solution in advance. This study drew a novel assessment and selection for smart agriculture solutions using an information error‐based Pythagorean fuzzy cloud algorithm. First, an evaluation index system built on smart agriculture solutions was constructed from four aspects. Then, a new concept of Pythagorean fuzzy clouds was defined to express the evaluation information for each indicator. Simultaneously, the Pythagorean fuzzy cloud weighted Bonferroni mean (PFCWBM) operator was developed to aggregate the assessment information of multiple indicators. Next, an assessment and selection decision framework for smart agriculture solutions based on the PFCWBM operator was presented. In addition, an example was given to illustrate the effectiveness of the proposed algorithm. Finally, a discussion was conducted to verify the superiority of our approach. The results showed that our algorithm can characterize and evaluate complex information and has high sensitivity and environmental adaptability.

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Section: Analysis Of Examplesmentioning

confidence: 99%

Section: Analysis Of Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment and selection of smart agriculture solutions using an information error‐based Pythagorean fuzzy cloud algorithm

Yang

Lin

et al. 2021

Int J of Intelligent Sys

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“…In other cases and contexts there is not a convergent view about the CSA practices between the different stakeholders [54]. The perceptions of the diverse actors about the smart agriculture have here their impacts [161]. It is important, namely through the extension services to assess and work the perceptions of the farmers about the smart practices in the farms, because this is decisive to achieve the objectives intended by the governments, including in terms of sustainability.…”

Section: References Particularities and Solutionsmentioning

confidence: 99%

Integrated-Smart Agriculture: Contexts and Assumptions for a Broader Concept

Martinho

Guiné

2021

Agronomy

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The innovative technologies developed in the different fields of science (nanotechnology, artificial intelligence, genetic modification, etc.) opened new and infinite possibilities for the several stakeholders that carry out their activities in the different economic sectors. For agriculture, these new approaches are particularly relevant and may bring interesting contributions, considering the specificities of the sector, often dealing with contexts of land abandonment and narrow profit margins. Nonetheless, the question in these unstopped evolutions is about the interlinkages with sustainability. In this context, the objectives of this study are to highlight the main insights from the available scientific literature about the interrelationships between the new trends in the agriculture and the sustainability. To achieve these aims, a search on the Web of Science Core Collection (WoS) and Scopus databases was carried out, on 15 May 2021, for the topics ‘smart agriculture’ and ‘sustainability’. A total of 231 documents (102 from WoS and 129 from Scopus) were obtained, remaining 155 documents after removing the duplicated, which were surveyed through systematic review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach. As main insights, the concerns of the researchers with the impacts on the sustainability from the transformations in the farming organization are highlighted. On the other hand, it was shown the relevance and the new opportunities, including in terms of food supply, arising from the precision agriculture, agricultural intelligence, vertical/urban farming, circular economy, internet of things, and crowdfarming. We suggest the new and wider concept of ‘integrated-smart agriculture’, better than ‘climate-smart agriculture’.

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“…With the study, the social media usage purposes of the farmers involved in agricultural production were determined. In the literature, there are studies on the social media usage purposes of farmers (Thakur et al, 2017;Soylu et al, 2016;Han et al, 2021;Kanjina, 2021;Ofori & El-Gayar, 2019;Riley & Robertson, 2021). However, no study was found for Konya province.…”

Section: Introductionmentioning

confidence: 99%

Social Media Usage Purposes of Farmers in Selçuklu District of Konya Province

Ögür

Karatekin

Doğançukuru³

2022

Turkish JAF Sci.Tech.

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The aim of this study is to determine the purpose of social media usage of farmers in Selçuklu district of Konya province. The sample volume was determined as 80 according to the stratified random sampling method, with a 95% confidence interval and with a 5% margin of error. Factor analysis was conducted to determine the farmers' social media usage purposes. Factor analysis was conducted on 19 independent variables and 3 independent variables were identified to determine the reasons for using social media. These variables are called professional development, socialization, and communication. The relationship between professional development, socialization, and communication variables and social media platforms was determined by linear regression analysis. For platforms used for socialization purposes, it was found that Facebook was statistically significant at 1% and Instagram was 5%. According to the regression analysis, platforms used for vocational development purposes, it was found that WhatsApp was statistically significant at 1% and YouTube was 5%. In social media, training, extension, projects, and promotions should be more widely supported in order to support the professional training of enterprises. In addition, pieces of training should be provided for enterprises to use social media platforms more effectively.

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The State and Future of Smart Agriculture: Insights from mining social media

Cited by 7 publications

References 31 publications

Assessment and selection of smart agriculture solutions using an information error‐based Pythagorean fuzzy cloud algorithm

Assessment and selection of smart agriculture solutions using an information error‐based Pythagorean fuzzy cloud algorithm

Integrated-Smart Agriculture: Contexts and Assumptions for a Broader Concept

Social Media Usage Purposes of Farmers in Selçuklu District of Konya Province

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