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IoT-Based Strawberry Disease Prediction System for Smart Farming

1
loT Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
2
Department of Agricultural Engineering, National Institute of Agricultural Sciences, Jeollabuk-do 55365, Korea
3
Department of Information and Communications Engineering, Sunchon National University, Jeollanam-do 57922, Korea
*
Authors to whom correspondence should be addressed.
Sensors 2018, 18(11), 4051; https://doi.org/10.3390/s18114051
Received: 15 October 2018 / Revised: 14 November 2018 / Accepted: 15 November 2018 / Published: 20 November 2018
(This article belongs to the Special Issue Internet-of-Things for Precision Agriculture (IoAT))
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PDF [3941 KB, uploaded 20 November 2018]
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Abstract

Crop diseases cannot be accurately predicted by merely analyzing individual disease causes. Only through construction of a comprehensive analysis system can users be provided with predictions of highly probable diseases. In this study, cloud-based technology capable of handling the collection, analysis, and prediction of agricultural environment information in one common platform was developed. The proposed Farm as a Service (FaaS) integrated system supports high-level application services by operating and monitoring farms as well as managing associated devices, data, and models. This system registers, connects, and manages Internet of Things (IoT) devices and analyzes environmental and growth information. In addition, the IoT-Hub network model was constructed in this study. This model supports efficient data transfer for each IoT device as well as communication for non-standard products, and exhibits high communication reliability even in poor communication environments. Thus, IoT-Hub ensures the stability of technology specialized for agricultural environments. The integrated agriculture-specialized FaaS system implements specific systems at different levels. The proposed system was verified through design and analysis of a strawberry infection prediction system, which was compared with other infection models. View Full-Text
Keywords: smart farming; prediction; infection forecast model; IoT; oneM2M; LoRa smart farming; prediction; infection forecast model; IoT; oneM2M; LoRa
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Kim, S.; Lee, M.; Shin, C. IoT-Based Strawberry Disease Prediction System for Smart Farming. Sensors 2018, 18, 4051.

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