# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 3 Application Areas
### Disease Diagnosis
#### 3.3.3 Invasive Insect-Pest Infestations  
In modern agriculture, electronic noses are indispensable tools, particularly in combating invasive insect-pest infestations. By discerning unique odor signatures emitted by distressed plants, electronic noses empower farmers to swiftly address issues, leading to minimized crop losses and reduced dependence on chemical interventions. This not only aligns with sustainable practices but also significantly bolsters overall food security. These electronic noses differentiate between healthy and diseased plants by analyzing volatile organic compounds (VOCs) emitted in response to stress, pathogens, or insect damage. These VOCs create distinct profiles, with specific compounds such as methyl salicate, jasmonic acid, and ethylene serving as reliable biomarkers for plant health [195]. Utilizing sensor arrays, e-noses detect VOCs and generate unique patterns for different samples, enabling the identification of characteristic differences between healthy and diseased plants. The prevalence of bacterial plant diseases, including common pathogens like Pseudomonas syringae pathways, Ralstonia solanacearum, and Agrobacteriumium tumefaciens [57], poses a significant global challenge, causing substantial annual economic losses. Furthermore, the presence of quarantine pathogens escalates the issue, as their eradication is mandated by European Union directives, imposing additional financial burdens. Consequently, there is an urgent need for sophisticated analytical tools capable of promptly identifying diseasesin their early stages. Several studies have highlighted the potential of electronic noses in addressing these challenges. [PERSON] et al. [196] utilized a commercial electronic nose (PEN 3) to differentiate between healthy and infected potatoes affected by Ralstonia solanacearum or Claviobacter michi-ganensis ssp. Their work focused on optimizing gas sampling methods, highlighting the effectiveness of VOC-sorbent cartridges in enhancing sensitivity by concentrating volatile compounds. Importantly, they discovered that passive gas sampling, when compared to active sampling, achieved higher classification accuracy. A pivotal discovery in their research was the influence of sensor response correlation. Through the strategic exclusion of highly correlated sensor responses using PCA loading plots, the researchers achieved a substantial enhancement in classification results. This underscores the critical importance of selecting uncorrelated sensor responses for constructing effective classification models. Additionally, their study showcased the potential of PCA score plots to reflect disease incidence and severity, providing valuable insights into disease characterization. Furthermore, [PERSON] et al. [196] introduced an innovative method for assessing disease severity by creating a five-class phytopathogenicer ladder based on visual sample analysis. This novel approach not only enriched their findings but also introduced a tool for evaluating disease severity within similar contexts. [PERSON] et al. [197] conducted research on potato soft rot disease severity detection using five MOX sensors. They introduced an optimized bionic electronic nose gas chamber and a well-designed sampling device to effectively detect changes in volatile substances associated with the infected soft rot disease in potato tubers. Through the utilization of the RBF neural network and SVM algorithms, they successfully detected and identified the presence of soft rot disease in potato tuber samples. The results showcased that their proposed bionic electronic nose system holds potential for early disease detection. Notably, the SVM algorithm demonstrated a remarkable recognition rate of up to 89.7%, outperforming the RBF algorithm. This research addresses the need for sensitive and rapid disease detection methods, offering the potential to significantly impact potato industry practices and economics. In a significant application of electronic nose technology, the work of [PERSON] et al.