Electronic nose filtering technique optimisation for pepper yellow leaf curl virus detection

Abstract

The detection of virus infection attacking plants mainly depends on polymerase chain reaction (PCR) testing. Nevertheless, the COVID-19 pandemic, during which the availability of the PCR test was limited, highlights the need for reliable alternative methods for detecting viruses. An effective technique for diagnosing plant disease involves the use of an electronic nose (e-nose) that can detect volatile organic compounds (VOCs) emitted by plants. However, the extensive use of e-noses is limited by the noise that can come from temperature and humidity changes. In order to address this limitation, this research focused on optimising filtering techniques to improve e-nose performance in detecting pepper yellow leaf curl virus (PYLCV) infected chilli plants. The samples were taken from commercial plantations, ensuring that those infected grew in a controlled environment, and ensuring PYLCV detection in diverse conditions. The methods of Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), and Savitzky-Golay (SG) filtering were used for the purpose of noise filtering. The optimisation of each filtering technique was performed, such as cutoff frequency for the FFT, the decomposition levels and types of mother wavelets for the DWT, and the polynomial degree and number of windows for the SG filter. The optimisation was performed using a deep neural network (DNN). As a result, the DWT symlet4 level 10 with a specific filter length outperformed the FFT and SG method, with DNN accuracy reaching 97.8% and increasing the accuracy of the unfiltered signal by 5.4%. The result was then validated with other classification models. This proves that with a suitable filtering technique, the e-nose can be a reliable instrument for plant disease detection.