Cutting-Edge Techniques Highlighted for Disease Detection and Classification in Solanaceae Crops

Solanaceae crops such as tomato, potato, and pepper are vital to global food security but remain vulnerable to numerous plant diseases. Early and accurate disease detection is critical for sustainable crop management. Recent advancements have introduced cutting-edge techniques that significantly enhance disease diagnosis and classification. Key technologies include: (a) Hyperspectral Imaging (HSI), which enables early disease detection through spectral pattern analysis, and (b) Deep Learning-based Convolutional Neural Networks (CNNs), known for high accuracy in image-based classification. Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors facilitate large-scale field monitoring. Additionally, Internet of Things (IoT)-based Smart Sensing Systems provide real-time data from field conditions, while CRISPR-based Molecular Diagnostics offer ultra-sensitive, gene-targeted pathogen detection. Integrating these techniques can revolutionize plant health monitoring, reduce pesticide misuse, and increase crop yields. This paper reviews current advancements and their synergistic application in Solanaceae crop disease management for precision agriculture.