Early Warning System Using Change Point Analysis to Detect Microclimate Anomalies

Abstract

The agricultural sector is required to provide food products for human needs. To increase agricultural yields, precision agriculture approaches are required by the utilization of information and technology to maximize agricultural productivity. Precision agriculture systems cannot be separated from monitoring and controlling the environment. This system is necessary to keep the surrounding environment or microclimate by plants requirements. However, during the monitoring and control process, some failures may occur due to technical and non-technical problems, and they will cause damage if not treated immediately. Therefore, to keep the microclimate under control according to plant growing requirements, an early warning system is necessary. The objective of this study was to develops an early warning system for microclimate anomalies using Change Point Analysis based on evapotranspiration calculations. This system works to detect changes in microclimate anomalies caused by malfunctions in the monitoring or control system. Microclimate time-series data obtained from monitoring in the growth chamber are used to calculate evapotranspiration. To represent the environmental condition inside the systems, reference evapotranspiration time-series data estimated from climate data using Penmann-Monteith 56 model, were analyzed using Singular Spectrum Transformation (SST) to obtain the change point score. As the result of the performance test and observation, microclimate anomalies inside the growth chamber could be detected by the change point detection representing by the change point score.

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