欧亚国际

来自以色列的科学家利用Plant逆境生物学研究系统在知名期刊Cell上发表了题为“Sounds emitted by plants under stress are airborne and informative”的文章。Cell是业界知名期刊，影响因子高达66.85。这也是利用Plantarray系统今年发表的第10篇文章。

Plantarray是一款基于称重的高通量、多传感器生理表型平台以及植物逆境生物学研究通用平台。 该系统可持续、实时测量位于不同环境条件下、阵列中每个植株的土壤-植物-空气（SPAC）中的即时水流动。 直接测量根系和茎叶系统水平衡和生物量增加，计算植物生理参数以及植物对动态环境的反馈。 系统以有效、易用、无损的方式针对植物对不同处理的反应、预测植物生长和生产力进行定量比较，广泛应用于生物胁迫和非生物胁迫以及植物栽培加速育种研究等，胁迫研究涵盖干旱胁迫、盐胁迫、重金属胁迫、热、冷胁迫、光胁迫以及灌溉/养分、CO2指示、植物健康等领域的研究。

植物在胁迫下发出的声音是通过空气传播且信息丰富

热点

植物在受到胁迫时会发出超声波空气传播的声音

发出的声音揭示了植物的类型和状况

可以在温室环境中检测和解释植物的声音

总结

受到胁迫的植物表现出改变的表型，包括颜色、气味和形状的变化。然而，以前还没有人研究过胁迫植物发出的在空气传播的声音。在这里，我们展示了胁迫植物发出的通过空气传播的声音，这些声音可以从远处记录下来并分类。我们在声学室内和温室中记录了番茄和烟草植物发出的超声波，同时监测了植物的生理参数。我们开发了机器学习模型，仅根据发出的声音就成功地识别了植物的状况，包括脱水水平和损伤。这些信息性的声音也可能被其他生物检测到。这项工作为了解植物及其与环境的相互作用开辟了途径，并可能对农业产生重大影响。

关键词

植物生物声学、植物声学、空气传播声音、信号、植物通信、干旱胁迫、应激反应、机器学习、人工智能、植物远程监测

Sounds emitted by plants under stress are airborne and informative: Cell

DOI:http://doi.org/10.1016/j.cell.2023.03.009

Highlights

Plants emit ultrasonic airborne sounds when stressed

The emitted sounds reveal plant type and condition

Plant sounds can be detected and interpreted in a greenhouse setting

Summary

Stressed plants show altered phenotypes, including changes in color, smell, and shape. Yet, airborne sounds emitted by stressed plants have not been investigated before. Here we show that stressed plants emit airborne sounds that can be recorded from a distance and classified. We recorded ultrasonic sounds emitted by tomato and tobacco plants inside an acoustic chamber, and in a greenhouse, while monitoring the plant’s physiological parameters. We developed machine learning models that succeeded in identifying the condition of the plants, including dehydration level and injury, based solely on the emitted sounds. These informative sounds may also be detectable by other organisms. This work opens avenues for understanding plants and their interactions with the environment and may have significant impact on agriculture.

Summary

Stressed plants show altered phenotypes, including changes in color, smell, and shape. Yet, airborne sounds emitted by stressed plants have not been investigated before. Here we show that stressed plants emit airborne sounds that can be recorded from a distance and classified. We recorded ultrasonic sounds emitted by tomato and tobacco plants inside an acoustic chamber, and in a greenhouse, while monitoring the plant’s physiological parameters. We developed machine learning models that succeeded in identifying the condition of the plants, including dehydration level and injury, based solely on the emitted sounds. These informative sounds may also be detectable by other organisms. This work opens avenues for understanding plants and their interactions with the environment and may have significant impact on agriculture.

Keywords

plant bioacoustics，phytoacoustics，airborne sound，signaling，plant communication，drought stress，stress responses，machine learning，artificial intelligence，plant remote monitoring