IoT-Based Strawberry Disease Prediction System for Smart Farming

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.

Systematic review of Internet of Things in smart farming

“…Kim et al 84 proposed disease predication system for strawberry plant. The IoT systems were based on oneM2M platform and consist of LoRa Class C devices and gateway.…”

Swarm-Intelligence-Centric Routing Algorithm for Wireless Sensor Networks

“…In other words, the sensing information received from each node can be merged in the WSN relay node and delivered as a single dataset. For example, in the case of a WSN that measures the maximum/minimum temperature of a smart greenhouse, the relay node can be considered efficient when it exclusively transmits the said maximum/minimum temperature value from among several values received by multiple sensor nodes [ 19 , 20 ]. In several scenarios involving WSNs, the use of a data merging technique can reduce the data throughput and increase the network survival time.…”

A Comprehensive Study of the Use of LoRa in the Development of Smart Cities

“…The data generated by sensors can be processed by cloud or edge computing [24]. Additionally, it is possible to determine adverse effects on the plantation by using mathematical algorithms as time series [25].…”

“…Some aspects that are important when using sensors are (i) timestamp and (ii) sensor identification. The timestamp between a node and the gateway must be synchronized; this allows validation of the information received by the nodes as new or old [25]. For sensor identification, a Local ID could be used.…”