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Taking tomato variety of ‘Jiali14’ as experimental materials, the relationship among the acoustic emissions signal, soil water contents and photosynthetic characteristic parameters of tomato under water deficit was analyzed. The short-time Fourier transform and power spectrum analysis computed by LabVIEW 2014 were employed to examine the spectrum characteristics of the collected AE signals. The whole process of water deficit was approximately divided into four stages: DAY1, DAY2, DAY3 and DAY4 according to the soil water contents. The results showed that characteristics for AEs during the water deficit period were presented by a regular change pattern, while the peak time was 10:00—16:00 and the amplitude was 40~60dB. Power spectrum for acoustic emissions of tomato was a phenomenon observed in characteristic parameters with dominant frequency (fp), central frequency (fa) and formants. The frequencies of fp, fa and the second, third resonance peaks of DAY1 were distributed at 250~375kHz, where the first resonance peak of DAY1 was distributed mainly at 0~125kHz. The first, second and third resonance peaks of DAY2, DAY3 and DAY4 were all centralized at 250~375kHz. From the amplitude of resonance peaks, the arrangement of DAY1 was the third, second, and first formant, but the order of formants for DAY2 and DAY4 were opposite of DAY1. A single peak curve was existed with net photosynthetic rate of tomato, and the peak value Pn occurred at 12:00, whereas the intercellular CO2 concentrations were increased firstly and then decreased with the decrease of soil water contents. Although the changes of amplitude and frequencies of resonance peaks for acoustic emissions of tomato in initial irrigation stage and late irrigation stage were different, a good correspondence between AE outcomes and experimental observations of the net photosynthetic rate, intercellular CO2 concentrations and photosynthetic performances was obtained and discussed. This result may provide a new monitoring method for acoustic emissions characteristics of water deficit through power spectrum analysis.