Abstract In cultures like grapevine, growth, yield and fruit quality depend on precise regulation of water stress using precision irrigation. In arid climates with seasonal droughts, karst areas and porous stony soils it is difficult to assess irrigation thresholds only based on soil water balance, requiring novel, plant-based techniques of early drought stress detection. In plants, water stress causes an increase in evapotranspiration rate, subjecting the water in the xylem conduits to high tension. This creates cavitations, leading to xylem embolization, loss of hydraulic conductivity and possibly plant death. A promising emerging technology for automated, non-invasive field monitoring of xylem cavitations is acoustic emission (AE) testing. Passive piezoelectric sensors on trunk are used to capture intermittently appearing AE, occurring in ultrasonic frequency-band from 100 kHz to 1 MHz. To make AE suitable as an input for precision irrigation, we present a novel AE time-frequency feature set for discrimination between normal and drought stress conditions. The proposed feature extraction method entails software-compensation of sensor's frequency response, enabling the wide-band frequency-analysis with miniature resonant high-frequency piezoelectrics. The signal analysis revealed three characteristic groups of AEs, differentiated by their spectral composition: single-component, multi-component, and broadband AEs. In conditions of increased water stress, single-component emissions cluster around 200 kHz, and multi-component emissions group between 200 and 600 kHz, forming discriminant time-frequency signatures. The study entails an extensive set of laboratory pot-plant dehydration experiments on four different grapevine cultivars, verifying the selectivity, sensitivity and repeatability of the extracted feature set. It proved sensitive enough for detection of drought stress corresponding to moderate leaf water potentials ranging between −0.7 and −1.2 MPa.

中文翻译：

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用于检测干旱胁迫的葡萄木质部声发射的时频特征

摘要 在葡萄藤等文化中，生长、产量和果实质量取决于使用精确灌溉对水分胁迫的精确调节。在具有季节性干旱、喀斯特地区和多孔石质土壤的干旱气候中，仅根据土壤水分平衡很难评估灌溉阈值，需要基于植物的新型早期干旱胁迫检测技术。在植物中，水分胁迫导致蒸发蒸腾速率增加，使木质部管道中的水承受高压。这会产生空化，导致木质部栓塞、导水率损失和可能的植物死亡。一种用于木质部空化的自动化、非侵入性现场监测的有前途的新兴技术是声发射 (AE) 测试。躯干上的无源压电传感器用于捕捉间歇性出现的AE，发生在 100 kHz 至 1 MHz 的超声频带中。为了使 AE 适合作为精确灌溉的输入，我们提出了一种新的 AE 时频特征集，用于区分正常和干旱胁迫条件。所提出的特征提取方法需要对传感器的频率响应进行软件补偿，从而能够使用微型谐振高频压电进行宽带频率分析。信号分析揭示了 AE 的三个特征组，根据它们的光谱组成进行区分：单组分、多组分和宽带 AE。在水资源压力增加的情况下，单分量排放集中在 200 kHz 附近，多分量排放组在 200 至 600 kHz 之间，形成可区分的时频特征。该研究需要对四种不同的葡萄品种进行广泛的实验室盆栽脱水实验，以验证提取的特征集的选择性、灵敏度和可重复性。事实证明，它对于检测与介于 -0.7 到 -1.2 MPa 之间的中等叶水势相对应的干旱胁迫足够敏感。