Development of a digital nose system for early detection of plant stress

Early forest stress detection is essential for the preservation of healthy forest ecosystems. The aim of this study is to develop an electronic nose (e-nose) using metal oxide (MOx) gas sensors that can differentiate between stressed and healthy trees by detecting volatile organic compounds (VOCs). An Arduino microcontroller was used to collect data from the gas sensors, while Python was implemented for data processing. The system applied machine learning algorithms such as Linear Discriminant Analysis (LDA), as a supervised learning method, and Principal Component Analysis (PCA), as an unsupervisded learning method, to classify and perform dimensionality reduction on the sensor data. To enhance portability and usability, a printed circuit board (PCB) was designed, creating a compact and efficient e-nose for field testing. The sensor array was tested with various materials found in stressed trees. PCA was also applied to assess sensor sensitivity and evaluate sensor configurations. Initial results demonstrated the e-nose’s ability to distinguish between diseased and healthy trees with significant accuracy. PCA showed good separation of VOC patterns but lower accuracy when detecting multiple target gases. LDA provided clearer distinctions between the two classes with minimal overlap. Although MOx sensors exhibited high sensitivity, their low selectivity for specific gases affected classification accuracy. The high sensitivity of MOx sensors often comes at the expense of selectivity. Future research will focus on identifying specific VOCs emitted by stressed trees using neural networks and improving the e-nose’s ability to detect a wider range of compounds.