A Study on Acer Mono Sap Integration Management System Based on Energy Harvesting Electric Device and Sap Big Data Analysis Model

• Main Authors: Se-Hoon Jung, Jun-Yeong Kim, Jun Park, Jun-Ho Huh, Chun-Bo Sim
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Electronics
• Subjects: big data collection; Acer mono sap; energy harvesting; ANN; SVM; regression analysis
• Online Access: https://www.mdpi.com/2079-9292/9/11/1979

This study set out to invent an Information and Communication Technologies (ICT)-based smart Acer mono sap collection electric device to make efficient use of the labor force by reducing inefficient activities of old manual work to record sap exudation and state information. Based on the assumption that environmental information would have close connections with Acer mono sap exudation to reinforce the competitive edge of production in forest products, the study analyzed correlations between Acer mono sap exudation and environmental information and predicted Acer mono exudation. A smart collection of electric devices would gather data about Acer mono sap exudation per hour on outdoor temperature, humidity, conductivity, and wind direction and velocity, and was installed in four areas in the Republic of Korea, including Sancheong, Gwangyang, Geoje, and Inje. Collected data were used to analyze correlations between environmental information and Acer mono sap exudation using four different algorithms, including linear regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), and random forest, to predict Acer mono sap exudation. Remarkable outcomes were obtained across all the algorithms except for linear regression, demonstrating close connections between environmental information and Acer mono sap exudation. The random forest model, which showed the most outstanding performance, was used to make a mobile app capable of providing predicted Acer mono sap exudation and collected environmental information.