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2020-03-12
Gawanmeh, Amjad, Parvin, Sazia, Venkatraman, Sitalakshmi, de Souza-Daw, Tony, Kang, James, Kaspi, Samuel, Jackson, Joanna.  2019.  A Framework for Integrating Big Data Security Into Agricultural Supply Chain. 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService). :191–194.

In the era of mass agriculture to keep up with the increasing demand for food production, advanced monitoring systems are required in order to handle several challenges such as perishable products, food waste, unpredictable supply variations and stringent food safety and sustainability requirements. The evolution of Internet of Things have provided means for collecting, processing, and communicating data associated with agricultural processes. This have opened several opportunities to sustain, improve productivity and reduce waste in every step in the food supply chain system. On the hand, this resulted in several new challenges, such as, the security of the data, recording and representation of data, providing real time control, reliability of the system, and dealing with big data. This paper proposes an architecture for security of big data in the agricultural supply chain management system. This can help in reducing food waste, increasing the reliability of the supply chain, and enhance the performance of the food supply chain system.

2017-12-28
Suebsombut, P., Sekhari, A., Sureepong, P., Ueasangkomsate, P., Bouras, A..  2017.  The using of bibliometric analysis to classify trends and future directions on \#x201C;smart farm \#x201D;. 2017 International Conference on Digital Arts, Media and Technology (ICDAMT). :136–141.

Climate change has affected the cultivation in all countries with extreme drought, flooding, higher temperature, and changes in the season thus leaving behind the uncontrolled production. Consequently, the smart farm has become part of the crucial trend that is needed for application in certain farm areas. The aims of smart farm are to control and to enhance food production and productivity, and to increase farmers' profits. The advantages in applying smart farm will improve the quality of production, supporting the farm workers, and better utilization of resources. This study aims to explore the research trends and identify research clusters on smart farm using bibliometric analysis that has supported farming to improve the quality of farm production. The bibliometric analysis is the method to explore the relationship of the articles from a co-citation network of the articles and then science mapping is used to identify clusters in the relationship. This study examines the selected research articles in the smart farm field. The area of research in smart farm is categorized into two clusters that are soil carbon emission from farming activity, food security and farm management by using a VOSviewer tool with keywords related to research articles on smart farm, agriculture, supply chain, knowledge management, traceability, and product lifecycle management from Web of Science (WOS) and Scopus online database. The major cluster of smart farm research is the soil carbon emission from farming activity which impacts on climate change that affects food production and productivity. The contribution is to identify the trends on smart farm to develop research in the future by means of bibliometric analysis.