An Integrated IoT Sensor-Camera System toward Leveraging Edge Computing for Smart Greenhouse Mushroom Cultivation

Hoang Hai Nguyen, Dae-Yun Shin, Woo-Sung Jung, Tae-Yeol Kim and Dae-Hyun Lee ()
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Hoang Hai Nguyen: Sejong Rain Co., Ltd., In-House Venture of K-Water, Daejeon 34134, Republic of Korea
Dae-Yun Shin: Sejong Rain Co., Ltd., In-House Venture of K-Water, Daejeon 34134, Republic of Korea
Woo-Sung Jung: K-Water Research Institute, Daejeon 34045, Republic of Korea
Tae-Yeol Kim: Graduate School of Smart Agriculture, Chungnam National University, Daejeon 34134, Republic of Korea
Dae-Hyun Lee: Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea

Agriculture, 2024, vol. 14, issue 3, 1-21

Abstract: Industrial greenhouse mushroom cultivation is currently promising, due to the nutritious and commercial mushroom benefits and its convenience in adapting smart agriculture technologies. Traditional Device-Cloud protocol in smart agriculture wastes network resources when big data from Internet of Things (IoT) devices are directly transmitted to the cloud server without processing, delaying network connection and increasing costs. Edge computing has emerged to bridge these gaps by shifting partial data storage and computation capability from the cloud server to edge devices. However, selecting which tasks can be applied in edge computing depends on user-specific demands, suggesting the necessity to design a suitable Smart Agriculture Information System (SAIS) architecture for single-crop requirements. This study aims to design and implement a cost-saving multilayered SAIS architecture customized for smart greenhouse mushroom cultivation toward leveraging edge computing. A three-layer SAIS adopting the Device-Edge-Cloud protocol, which enables the integration of key environmental parameter data collected from the IoT sensor and RGB images collected from the camera, was tested in this research. Implementation of this designed SAIS architecture with typical examples of mushroom cultivation indicated that low-cost data pre-processing procedures including small-data storage, temporal resampling-based data reduction, and lightweight artificial intelligence (AI)-based data quality control (for anomalous environmental conditions detection) together with real-time AI model deployment (for mushroom detection) are compatible with edge computing. Integrating the Edge Layer as the center of the traditional protocol can significantly save network resources and operational costs by reducing unnecessary data sent from the device to the cloud, while keeping sufficient information.

Keywords: smart agriculture; mushroom; edge computing; farm management information system (FMIS); machine vision; Agricultural IoT (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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