Smartphones have become a useful tool in agriculture because their mobility matches the nature of farming, the cost of the device is highly accessible, and their computing power allows a variety of practical applications to be created. Moreover, smartphones are nowadays equipped with various types of physical sensors which make them a promising tool to assist diverse farming tasks. This paper systematically reviews smartphone applications mentioned in research literature that utilize smartphone built-in sensors to provide agricultural solutions. The initial 1,500 articles identified through database search were screened based on exclusion criteria and then reviewed thoroughly in full text, resulting in 22 articles included in this review. The applications are categorized according to their agricultural functions. Those articles reviewed describe 12 farming applications, 6 farm management applications, 3 information system applications, and 4 extension service applications. GPS and cameras are the most popular sensors used in the reviewed papers. This shows an opportunity for future applications to utilize other sensors such as accelerometer to provide advanced agricultural solutions.

Pose estimation has various applications in analyzing human body movement and behavior, including providing feedback to users about their movements so they can adjust and improve their movement skills. To investigate the current research status and possible gaps, we searched Scopus and Web of Science for articles that (1) human 'body' pose estimation is used and (2) user movement is assessed and communicated. We used either a bottom-up or top-down approach to analyze 45 articles for methods used to estimate human body pose, assess movement, provide feedback to users, as well as methods to evaluate them. Our review found that pose estimation systems typically used CNNs while movement assessment methods varied from mathematical formulas or models, rule-based approaches, to machine learning. Feedback was primarily presented visually in verbal forms and nonverbal forms. The experiments to evaluate each part ranged from the use of public datasets to human participants. We found that pose estimation libraries play an important role in the advancement of this field. Nevertheless, the effectiveness and factors for choosing movement assessment methods for a new context are still unclear. In the end, we suggest that studies about feedback prioritization and erroneous feedback are needed.

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

A robust food traceability system is crucial for ensuring seamless information flow within supply chains. While the current centralised approach, typically led by a supply chain leader, facilitates information exchange, it faces significant limitations, such as prolonged tracing processes and a lack of transparency. Blockchain technology presents a promising solution to address these challenges. However, concerns persist about the costs of IT infrastructure required to operate blockchain-based systems. This research compares the costs between centralised and blockchain ecosystems, focusing on the total cost for the entire industry, using the Thai broiler supply chain as a case study. We analysed both centralised and blockchain IT models, selecting 13 deployment scenarios for comparison. The Thai broiler supply chain provided data on IT infrastructure usage, with Monte Carlo simulation employed to address undisclosed supply chain data. The IT infrastructure costs for each scenario were calculated and compared. The analysis reveals a significant finding: blockchain implementation results in approximately 43% lower infrastructure costs compared to the practical centralised approach. This cost-effectiveness suggests that third parties, such as government bodies or central regulatory agencies, could play a crucial role in supporting the transition from centralised to blockchain-based food traceability systems, thereby enhancing supply chain transparency, efficiency, and food safety.

Blockchain technology, a decentralized database that encourages collaboration, transparency, and security, is popular in Thailand, as seen from many practical projects. Nevertheless, information about Thailand’s blockchain ecosystem is incomplete. We conducted literature reviews of research articles, education courses, and industry projects, to explore blockchain from the technology adoption flow including academic research, knowledge acquisition, and project implementation. Blockchain research articles were collected from the Scopus database, while projects and courses were retrieved from an internet search. After applying inclusion and exclusion criteria, a total of 13 research articles, 50 courses, and 54 industry projects, dated from 2016 to 2020, were included in our review. Based on an analysis with respect to trends, focuses, and characteristics in all three aspects, we found that blockchain technology in Thailand was in its infancy, but has been increasing in numbers and domain varieties. However, blockchain technology did not move towards the same direction for all aspects. Most research articles were found in government services, supply chain, and traceability domains; while most projects were in the financial domains. We offer possible explanations for this observation. This paper provides challenges and opportunities for the research community and involved parties in practical implementation.

Pose estimation has various applications in analyzing human movement and behavior, including providing feedback to users about their movements so they could adjust and improve their movement skills. To investigate the current research status and possible gaps, we searched Scopus and Web of Science for articles that (1) human `body' pose estimation is used and (2) user movement is assessed and communicated. We used either a bottom-up or top-down approach to analyze 20 articles for methods used to estimate human pose, assess movement, provide feedback to users, as well as methods to evaluate them. Our review found that pose estimation systems typically used CNNs while movement assessment methods varied from mathematical formulas or models, rule-based approaches, to machine learning. Feedback was primarily presented visually in verbal forms and nonverbal forms. The experiments to evaluate each part ranged from the use of public datasets to human participants. We found that while there was an improvement, the majority of pose estimation challenges remain. The effectiveness and factors for choosing movement assessment methods for a new context are still unclear. In the end, we suggest that studies about feedback prioritization and erroneous feedback are needed.