Molecular Pathogens 2024, Vol.15, No.2, 93-105 http://microbescipublisher.com/index.php/mp 101 visible/near-infrared (Vis/NIR) spectroscopy and computer vision. These technologies allow for the on-line detection of fungal contamination in stored maize, providing a more accurate and timely assessment of infection levels. For instance, a study demonstrated that integrating Vis/NIR spectroscopy with computer vision achieved 100% accuracy in discriminating samples infected by different fungal strains, significantly improving the monitoring process (Shen et al., 2019). Moreover, field surveillance can be enhanced by employing hyperspectral imaging combined with multivariate image analysis. This method has proven effective in differentiating between Fusarium verticillioides and Fusariumgraminearum with high accuracy, sensitivity, and specificity. The non-invasive nature of hyperspectral imaging makes it a valuable tool for continuous monitoring of maize fields, allowing for early intervention and reducing the risk of widespread contamination (Conceição et al., 2020). These advanced surveillance techniques not only improve the accuracy of pathogen detection but also facilitate timely management decisions, ultimately protecting crop yield and quality. 7.2 Diagnostic tools and technologies The development of precise diagnostic tools is crucial for the early detection and quantification of Fusariumspp. and other fungal pathogens in maize. One of the most promising technologies is real-time PCR (qPCR) targeting the internal transcribed spacer (ITS) region. This method allows for the specific identification and quantification of multiple Fusarium species, including F. oxysporum, F. verticillioides, and F. graminearum, as well as Magnaporthiopsis maydis. The high sensitivity and specificity of qPCR make it an excellent tool for early diagnosis and certification purposes, ensuring that infected plants are identified and managed promptly (Campos et al., 2019). In addition to qPCR, near-infrared hyperspectral imaging (HSI-NIR) has emerged as a rapid and non-destructive diagnostic tool. By combining HSI-NIR with pattern recognition analysis, researchers have developed methods to accurately identify mycotoxin-producing Fusarium species in maize. This technology not only detects the presence of pathogens but also provides information on the level of contamination, enabling more effective management strategies (Conceição et al., 2020). The integration of these advanced diagnostic tools into routine monitoring programs can significantly enhance the early detection and control of fungal pathogens in maize. 7.3 Predictive modeling and risk assessment Predictive modeling and risk assessment are vital components of an integrated approach to managing Fusarium and other fungal pathogens in maize. These models use various data inputs, including weather conditions, crop management practices, and pathogen biology, to predict the likelihood of disease outbreaks. For example, studies have shown that weather conditions, such as heavy rainfall and high humidity, significantly influence the incidence of fungal diseases in maize (Czarnecka et al., 2022). By incorporating such environmental factors into predictive models, researchers can forecast disease risk and recommend timely interventions. Risk assessment tools also play a crucial role in identifying high-risk areas and periods for fungal contamination. For instance, a study on post-harvest practices in Vietnam highlighted the impact of traditional methods on the proliferation of Fusarium verticillioides and fumonisins in maize. The research identified specific practices that mitigate contamination, such as removing damaged cobs at harvest and drying maize on cement yards (Tran et al., 2020). By understanding the factors that contribute to fungal growth and mycotoxin production, predictive models can help farmers implement effective management practices, reducing the risk of contamination and ensuring the safety and quality of maize crops. 8 Concluding Remarks The integrated management of Fusariumand other fungal pathogens in maize has shown promising results across various studies. The application of insecticides significantly reduces insect damage, which in turn decreases Fusarium verticillioides infection and fumonisin contamination. Biological control methods, such as the use of
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