Cutting Edge Techniques for Disease Detection in Solanaceous Crops

Solanaceous crops, including tomatoes, potatoes, peppers, and eggplants, are key players in global agriculture, valued for their high yield potential and economic importance. However, these crops face significant threats from damaging diseases—ranging from fungal to bacterial and viral infections—that can drastically reduce yields and disrupt both food security and economic stability. As a result, swift and precise detection of plant diseases is pivotal for the development of successful management strategies. Yet, conventional methods, though widely adopted, often prove ineffective and lead to delays. This chapter explores cutting-edge technologies that enable rapid and precise detection of diseases in Solanaceae crops. It includes new methods like reverse transcription PCR (RT-PCR), reverse transcription recombinase amplification (RT-RPA), real-time PCR (qPCR), FTA-LAMP, Gold Nanoparticle-based biosensor, and Reverse Transcriptase-Helicase Dependent Isothermal Application (RT-HAD) assay, as well as artificial intelligence (AI) systems that use machine learning, deep learning, hybrid, and multilingual models. These approaches enable sensitive, rapid, accurate, and field-applicable diagnostics. Innovations like CRISPR/Cas12a-based assays and on-field molecular diagnostics further expand access in low-resource settings. The integration of AI with image analysis and mobile apps empowers real-time disease monitoring, revolutionizing crop protection strategies for sustainable agriculture. Therefore, these advanced technologies enable easy and quick detection of diseases, which is more beneficial for prevention and disease management. Further development on these approaches will also facilitate and pave a path for effective prevention and disease management, thus controlling yield loss in Solanaceous crops.