RLC Sensor with Data-Driven Distance Compensation for Plant Root Temperature Estimation

Abstract

Wireless temperature monitoring of plant roots remains challenging due to signal dependence on variable sensor-reader distance. To overcome this limitation, this paper proposes a wireless, battery-free RLC-based temperature sensing system incorporating a data-driven distance compensation framework based on a two-stage polynomial regression strategy. A multi-model is proposed to estimate temperature from resistance data, while a fourth-degree polynomial model is used to estimate the sensor-reader distance from self-inductance measurements. The final predicted temperature is obtained by linear interpolation. The model approach is developed using data collected from an inductanceto- digital converter (LDC1101) reader of an RLC sensor with a PT1000. Experimental results show that at a sensor-reader distance of 2 mm, the system achieves a root mean square error (RMSE) of 0.788 °C, with errors normally distributed near zero (σ = 0.705 °C), and an RMSE of 2.163 °C with an error distribution (σ = 1.24 °C) at a distance of 6 mm. This performance corresponds to a reduction in prediction error of up to 94% at short distances and over 80% at larger separations compared to a single global model.

Plant phenotyping relevance

植物根の温度という生理状態を測定する無線センサーと距離補償・推定手法を開発し、誤差で性能検証しているため、植物フェノタイピング手法が中心です。

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