Design of a Novel Remote Monitoring System for Smart Greenhouses Using the Internet of Things and Deep Convolutional Neural Networks

Adel Mellit, Mohamed Benghanem, Omar Herrak and Abdelaziz Messalaoui
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Adel Mellit: Renewable Energy Laboratory, Jijel University, Jijel 18000, Algeria
Mohamed Benghanem: Physics Department, Faculty of Science, Islamic University of Madinah, Madina 42351, Saudi Arabia
Omar Herrak: Renewable Energy Laboratory, Jijel University, Jijel 18000, Algeria
Abdelaziz Messalaoui: Renewable Energy Laboratory, Jijel University, Jijel 18000, Algeria

Energies, 2021, vol. 14, issue 16, 1-16

Abstract: To support farmers and improve the quality of crops production, designing of smart greenhouses is becoming indispensable. In this paper, a novel prototype for remote monitoring of a greenhouse is designed. The prototype allows creating an adequate artificial environment inside the greenhouse (e.g., water irrigation, ventilation, light intensity, and CO 2 concentration). Thanks to the Internet of things technique, the parameters controlled (air temperature, relative humidity, capacitive soil moisture, light intensity, and CO 2 concentration) were measured and uploaded to a designed webpage using appropriate sensors with a low-cost Wi-Fi module (NodeMCU V3). An Android mobile application was also developed using an A6 GSM module for notifying farmers (e.g., sending a warning message in case of any anomaly) regarding the state of the plants. A low-cost camera was used to collect and send images of the plants via the webpage for possible diseases identification and classification. In this context, a deep learning convolutional neural network was developed and implemented into a Raspberry Pi 4. To supply the prototype, a small-scale photovoltaic system was built. The experimental results showed the feasibility and demonstrated the ability of the prototype to monitor and control the greenhouse remotely, as well as to identify the state of the plants. The designed smart prototype can offer real-time remote measuring and sensing services to farmers.

Keywords: deep learning; Internet of things; mobile application; photovoltaic system; plant diseases classification; remote monitoring; smart greenhouse (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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