Characterization of abiotic stress of trees using AI methods on acoustic signals

Keywords : Data Science, ML & AI, Digital Agriculture, Remote Sensing (IoT and IoRT), Data quality, IoRT Network performance, Hybrid communication system

Abiotic stresses (e.g. frost, drought, wind) cause significant damage to natural and cultivated plants, which is expected to increase in the future with increasing climate variability (extreme climatic events).The detection of acoustic emissions is a promising way to measure continuously and non-invasively the damage affecting plants. Different sources of acoustic emissions have been identified (e.g. air bubble formation in conductive tissues, cell lysis, mechanical rupture, see references below) generating acoustic signals with their own characteristics. The analysis of the waveforms (amplitude, frequency, etc.) allows them to be discriminated under single stress conditions. However, to date, no study on a set of stresses (succession or interaction) has been carried out, and since plants are permanently subjected to different stresses, the use of this technique remains limited (in time, e.g. period of water stress, or in space, e.g. altitudinal limit). This case study therefore aims to better characterize the acoustic emissions generated by a single constraint and by their interactions, in order to ultimately develop a tool capable of measuring damage under natural conditions.

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