Integrated sensing and communication for lettuce water-status monitoring

Abstract

With the rapid development of smart agriculture, the agricultural Internet of Things (Ag-IoT) has gradually established a monitoring system centered on distributed sensing, low-power communication, and intelligent control. However, current solutions underexplore the perceptible characteristics of communication signals, and therefore do not fully utilize their latent potential in environmental perception. This paper targets the demand for crop water monitoring and introduces an integrated sensing and communication (ISAC) approach. This method can achieve non-contact and continuous perception of crop water status by reusing the communication link without altering the existing hardware architecture and frequency band configuration. Taking leafy vegetables such as lettuce as the research object, a prototype system based on a 3 GHz communication link was built. A quantitative mapping model between the water content of plant tissues and the amplitude and phase disturbances they cause to electromagnetic waves was established. A joint optimization mechanism that considers both communication performance and sensing accuracy was proposed to achieve a coordinated configuration between communication quality (BER < 10⁻⁴, SNR ≈ 20 dB, EVM < 8 %) and sensing accuracy (MAE = 2.51 %, R² = 0.92). Experiments were conducted in controlled environments and production-like scenarios, demonstrating that the method can stably identify the water status of lettuce while ensuring communication quality of service (QoS). The proposed ISAC method is potentially compatible with existing Ag-IoT frequency bands and physical-layer infrastructures, assuming access to pilot/CSI and airtime control. Protocol-level integration with LoRa, Wi-Fi, and NB-IoT is defined as future work It provides a low-cost, high-integration, and easily scalable communication-driven solution for water monitoring in smart agriculture.

Plant phenotyping relevance

レタスの水分状態という植物生理形質を、通信信号を再利用した非接触センシングで推定する方法を開発し、プロトタイプと精度検証を行っており、フェノタイピング手法が中心である。

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