Comparison of Smart Agriculture Literacy of Farmers in Thailand

Suebsombut, Paweena; Chernbumroong, Suepphong; Sureephong, Pradorn; Jaroenwanit, Pensri; Phuensane, Pongsutti; Sekhari, Aicha

doi:10.1109/ectidamtncon48261.2020.9090695

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…Some studies observed a relationship between Smart Farming Adoption and some critical variables: Firstly is the perception of economic and commercial barriers (Caffaro & Cavallo, 2019. Secondly is the farmers' education in technology (Pivoto et al, 2019;Suebsombut et al, 2020). Thirdly is smart technologies' perception of use and the potential for radical changes in farm management [6], [17].…”

Section: Aims and Objectivesmentioning

confidence: 99%

Opportunities and Barriers of Smart Farming Adoption by Farmers Based on a Systematic Literature Review

Sarmiento

Sánchez

Lamos-Díaz

2023

Proceedings INNODOCT/22. International Conference on Innovation, Documentation and Education

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Smart Farming is a revolutionary paradigm in the agri-food sector that integrates real-time data collection through various sensors and sources (i.e., the Internet of Things technologies (IoT) such as automation systems, farm bots, drones, and technological computer infrastructure). These integrated solutions support more intelligent decisions in the agricultural sector, increasing competitiveness and productivity in rural areas. However, there are difficulties with interoperability, security, data governance, farming practices diversity, farmer capacitation, and technology diffusion. End-users are heterogeneous, from illiterate producers to farm enterprises, which involves a custom ICT adoption strategy for each potential customer. This paper presents a systematic literature review that identifies the opportunities and barriers to adopting Smart Farming solutions in rural areas, highlighting the need to implement centered-user design strategies to increase the technology adoption considering two different types of farmers.

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Section: Aims and Objectivesmentioning

confidence: 99%

Opportunities and Barriers of Smart Farming Adoption by Farmers Based on a Systematic Literature Review

Sarmiento

Sánchez

Lamos-Díaz

2023

Proceedings INNODOCT/22. International Conference on Innovation, Documentation and Education

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“…As those papers are technologyoriented, they focus exclusively on providing a technical overview of the domain and/or tracking the most recent advancements in a field, giving little to no awareness to nonspecialists on technology integration guidelines. Other general survey papers focused on identifying trends and patterns of research on SF [45][46][47] or highlighting the role of different technologies in the agricultural industry in a particular region [48,49].…”

Section: Introductionmentioning

confidence: 99%

Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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As today's agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading the farming practices by shifting towards Smart Farming (SF) -utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify the suitable technologies and best practices for a particular application. This paper provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers' contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, the paper discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.

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“…The findings confirmed that Asian farmers are interested in technological devices. Meanwhile, a survey of smart agriculture literacy in aspect of farmers' experience and skills specific to Chiang Mai and Khon Kaen provinces in Thailand [7] revealed that farmers in Chiang Mai and Khon Kaen provinces have vastly different levels of smart agriculture literacy. As a result, the training section should take the participants' backgrounds into account.…”

Section: Introduction (Heading 1)mentioning

confidence: 99%

Training Evaluation in a Smart Farm using Kirkpatrick Model: A Case Study of Chiang Mai

Chernbumroong

Sureephong

Suebsombut

et al. 2022

2022 Joint International Conference on Digital Arts, Media and Technology With ECTI Northern Section Conference on Electrical,

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Farmers can now use IoT to improve farm efficiency and productivity by using sensors for farm monitoring to enhance decision-making in areas such as fertilization, irrigation, climate forecast, and harvesting information. Local farmers in Chiang Mai, Thailand, on the other hand, continue to lack knowledge and experience with smart farm technology. As a result, the 'SUNSpACe' project, funded by the European Union's Erasmus+ Program, was launched to launch a training course which improve the knowledge and performance of Thai farmers. To assess the effectiveness of the training, The Kirkpatrick model was used in this study. Eight local farmers took part in the training, which was divided into two sections: mobile learning and smart farm laboratory. During the training activities, different levels of the Kirkpatrick model were conducted and tested: reaction (satisfaction test), learning (knowledge test), and behavior (performance test). The overall result demonstrated the participants' positive reaction to the outcome. The paper also discusses the limitations and suggestions for training activities.

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Comparison of Smart Agriculture Literacy of Farmers in Thailand

Cited by 8 publications

References 9 publications

Opportunities and Barriers of Smart Farming Adoption by Farmers Based on a Systematic Literature Review

Opportunities and Barriers of Smart Farming Adoption by Farmers Based on a Systematic Literature Review

Technologies Driving the Shift to Smart Farming: A Review

Training Evaluation in a Smart Farm using Kirkpatrick Model: A Case Study of Chiang Mai

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