S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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S&M2708 Research Paper of Special Issue

Multi-sensor-based Environmental Forecasting System for Smart Durian Farms in Tropical Regions

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 based on the concept of the Internet of Things (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 Lee

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