pp. 3547-3561
S&M2708 Research Paper of Special Issue https://doi.org/10.18494/SAM.2021.3580 Published: October 20, 2021 Multi-sensor-based Environmental Forecasting System for Smart Durian Farms in Tropical Regions [PDF] Ping-Huan Kuo, Ren-Jean Liou, Pongpon Nilaphruek, Keeratiburt Kanchanasatian, Ting-Hao Chen, and Rong-Mao Lee (Received May 14, 2021; Accepted August 20, 2021) Keywords: smart farming, forecasting system, machine learning, CNN, SVM
Durians are among the most important fruit products in tropical countries. The environments of durians therefore must support a high yield to meet demand. Sunlight, temperature, and rainfall are all key variables, and any adverse factors will have a negative impact on production. We propose an environmental prediction system for a durian farm on the basis of the concept of the IoT. The system uses multiple machine learning algorithms to analyze collected environmental data and predict the next state of the environmental variables. From numerous experiments, our results show that the support vector machine (SVM) gives the best forecasts for temperature, whereas the convolutional neural network (CNN) performs best for predicting soil humidity. The results of this paper can provide farmers with real-time understanding of their farms and early warning of potential risks. The farm yield rates can hence be increased.
Corresponding author: Rong-Mao LeeThis work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Ping-Huan Kuo, Ren-Jean Liou, Pongpon Nilaphruek, Keeratiburt Kanchanasatian, Ting-Hao Chen, and Rong-Mao Lee, Multi-sensor-based Environmental Forecasting System for Smart Durian Farms in Tropical Regions, Sens. Mater., Vol. 33, No. 10, 2021, p. 3547-3561. |