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The presence of forest tree pathogens may lead to substantial problems and their early detection during seed storage or in nurseries may be critical for the choice of appropriate management strategy. A new construction of a low-cost electronic nose was tested on the samples of pathogenic fungi and oomycetes of <italic>Fusarium oxysporum</italic> and <italic>Phytopthora plurivora</italic>. The electronic nose uses Figaro Inc. TGS series sensors with applied heater voltage modulation. Such a mode of electronic nose operation may be more appropriate for application for constant monitoring of seeds storage, when we compare it to the method making use of modulation of the gas concentration. A rectangular shape of the sensors&#x2019; heater voltage modulation pattern, with a shallow drop of the heater voltage from the nominal voltage, was proposed. Data visualization using the principal component analysis method and the random forest machine learning technique was used to build classification models. A classification accuracy of 97&#x0025; was obtained by a fusion of data collected by TGS 2610 and TGS 2602 sensors.