Farmers’ Knowledge, Attitude, and Adoption of Smart Agriculture Technology in Taiwan

Chuang, Jui-Hsiung; Wang, Jiun-Hao; Liou, Yu-Chang

doi:10.3390/ijerph17197236

Cited by 54 publications

(39 citation statements)

References 20 publications

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“…Smart agricultural solutions emerge in an endless stream, but they also vary greatly because of the differences in cost input, technical performance, benefits of output, and service levels, 2,6 which makes it very difficult for enterprises or governments to choose a solution that meets their preferences. In the process of selection, different index systems reflect different preference characteristics of decision‐makers and then affect the final selection results 62 .…”

Section: The Evaluation Index System Built On Smart Agriculture Solut...mentioning

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.…”

Section: Introductionmentioning

confidence: 99%

“…One of the important means is to encourage agricultural enterprises to apply mature smart agricultural solutions to build a new form of smart agricultural development. Unfortunately, the constraints of the traditional agricultural management mode and the weak technical power of agricultural enterprises have made it very difficult for agricultural enterprises to introduce and implement agricultural smart solutions 2,6 . In particular, the current market is flooded with many smart agricultural solutions, and these solutions have obvious differences in technical performance, benefits, and management models, which create barriers to choice for each agribusiness with individualized needs.…”

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: The Evaluation Index System Built On Smart Agriculture Solut...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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“…15 For all these reasons, it has become a priority policy globally to raise awareness of farmers about Agriculture 4.0 technologies for agricultural applications. 16 Agriculture 4.0 is based upon smart use of data collected by advanced technologies mounted on satellites, tractors, robots, and drones. 17 It is also a growing area of research designed to figure out one of the most crucial challenges for humanity.…”

Section: Introductionmentioning

confidence: 99%

Assessing farmers' perception to Agriculture 4.0 technologies: A new interval‐valued spherical fuzzy sets based approach

Erdoğan

2021

Int J of Intelligent Sys

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Agriculture 4.0 is the usage of advanced technologies for ensuring sustainability and efficient use of resources in agriculture, which can be accepted one of the biggest challenges of mankind. With Agriculture 4.0 technologies, it is aimed to raise productivity, reduce waste and costs. To ensure that agriculture 4.0 technologies are adopted by farmers as soon as possible, the perspective and perception of farmers to these technologies should be analyzed firstly. For this purpose, we propose a decision-making framework to measure farmers' view of Agriculture 4.0 technologies and to perform a prioritization study based on the perception of use among these technologies. Multicriteria decision analysis is also utilized to deal with all qualitative and quantitative factors in the decision process. Within the scope of this study, interval-valued spherical fuzzy numbers are used to model the vagueness in the process in the best manner and to be able to reflect the uncertainty arising from the usage of linguistic variables in the decision process. The SWARA and MAIRCA multicriteria decision making (MCDM) methods, which have been used frequently in the literature and are applied very successfully in MCDM problems, have been firstly extended by spherical fuzzy sets (SFSs) and the advantages for these methods have been utilized within the framework of

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“…The region-wise study of farmers' response towards the adoptability of smart farming technologies [226,227]. Study of various types of urban agriculture [228] and business growth opportunities in urban indoor Vertical Farming [229].…”

mentioning

confidence: 99%

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Hati

Singh

2021

AgriEngineering

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Conventional farming necessitates a large number of resources and infrastructure such as land, irrigation, manpower to manage farms, etc. Modern initiatives are required to automate conventional farms. Smart indoor farms offer the potential to remedy the shortfalls of conventional farms by providing a controlled, intelligent, and smart environment. This paper presents a three-dimensional perspective consisting of soilless farming, energy harvesting, and smart technologies, which could be considered as the three important characteristics of smart indoor farms. A six-layer smart indoor farms architecture has also been proposed, which explains how data are collected using various sensors and devices and then transmitted onto the cloud infrastructure for further analysis and control through various layers. Artificial lighting, smart nutrition management, and artificial climate control, to name a few, are some of the important requirements for smart indoor farms while considering control and service management factors. The major bottleneck in installing such systems is both the economical and the technical constraints. However, with the evolution of technology (and when they become widely available in the near future), a more favourable farming scenario may emerge. Furthermore, smart indoor farms could be viewed as a potential answer for meeting the demands of a sustainable agricultural revolution as we move closer to Agriculture 4.0. Finally, in order to adapt smart indoor farms and their study scope, our work has presented various research areas to potential researchers.

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Farmers’ Knowledge, Attitude, and Adoption of Smart Agriculture Technology in Taiwan

Cited by 54 publications

References 20 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

Assessing farmers' perception to Agriculture 4.0 technologies: A new interval‐valued spherical fuzzy sets based approach

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

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