Towards Health Status Determination and Local Weather Forecasts from Vitis vinifera Electrome.

IF 3.9 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Alessandro Chiolerio, Federico Taranto, Giuseppe Piero Brandino
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Abstract

Recent advances in plant electrophysiology and machine learning suggest that bioelectric signals in plants may encode environmentally relevant information beyond physiological processes. In this study, we present a novel framework to analyse waveforms from real-time bioelectrical potentials recorded in vascular plants. Using a multi-channel electrophysiological monitoring system, we acquired continuous data from Vitis vinifera samples in a vineyard plantation under natural conditions. Plants were in different health conditions: healthy; under the infection of Flavescence dorée; plants in recovery from the same disease; and dead stumps. These signals were used as input features for an ensemble of complex machine learning models, including recurrent neural networks, trained to infer short-term meteorological parameters such as temperature and humidity. The models demonstrated predictive capabilities, with accuracy comparable to sensor-based benchmarks between one and two degree Celsius for temperature, particularly in forecasting rapid weather transitions. Feature importance analysis revealed plant-specific electrophysiological patterns that correlated with ambient conditions, suggesting the existence of biological pre-processing mechanisms sensitive to microclimatic fluctuations. This bioinspired approach opens new directions for developing plant-integrated environmental intelligence systems, offering passive and biologically rooted strategies for ultra-local forecasting-especially valuable in remote, sensor-sparse, or climate-sensitive regions. Our findings contribute to the emerging field of plant-based sensing and biomimetic environmental monitoring, expanding the role of flora to biosensors, useful in Earth system observation tasks.

葡萄健康状况测定及本地天气预报。
植物电生理学和机器学习的最新进展表明,植物的生物电信号可能编码超出生理过程的环境相关信息。在这项研究中,我们提出了一个新的框架来分析维管植物中记录的实时生物电位波形。利用多通道电生理监测系统,在自然条件下对葡萄种植园的葡萄样品进行连续监测。植物处于不同的健康状况:健康的;黄萎病感染;从同一疾病中恢复的植物;还有死树桩。这些信号被用作复杂机器学习模型集合的输入特征,包括循环神经网络,训练来推断短期气象参数,如温度和湿度。这些模型展示了预测能力,其精度可与基于传感器的1至2摄氏度的温度基准相媲美,特别是在预测快速天气变化方面。特征重要性分析揭示了植物特有的电生理模式与环境条件相关,表明存在对小气候波动敏感的生物预处理机制。这种受生物启发的方法为开发植物集成的环境智能系统开辟了新的方向,为超局部预测提供了被动的、基于生物的策略——在偏远、传感器稀少或气候敏感地区尤其有价值。我们的发现有助于基于植物的传感和仿生环境监测的新兴领域,将植物群的作用扩展到生物传感器,在地球系统观测任务中有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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