Bridging Artificial Intelligence, Machine Learning with Green Nanotechnology: A Visionary Framework for Smart Fungal...

Fungal diseases in plants pose significant threats to global food security and sustainable agriculture, yet current management methods remain limited by environmental concerns and increasing pathogen resistance. Advanced plant disease detection ML methods include hyperspectral imaging for early disease detection, deep learning (CNNs) for image-based classification, and Internet of Things (IoT) with edge AI for real-time monitoring. Although independent advances have been made in plant disease detection and nanotechnology-based control, there is a clear lack of integrated research that combines intelligent predictive systems with eco-friendly nanobiocontrol agents for comprehensive disease management. We hypothesize that an integrated framework linking advanced disease detection technologies with green-synthesized biowaste-derived nanoparticles will improve early diagnosis and sustainable control of fungal diseases compared to existing approaches. The objectives of this study are to (1) identify limitations in current detection and control strategies, (2) develop a hybrid system that couples real-time monitoring and prediction with targeted application of green nanomaterials, and (3) assess the efficacy of this integrated approach against representative fungal pathogens in model crops. This combined strategy aims to enhance precision, sustainability, and responsiveness in crop protection, offering a forward-looking paradigm for resilient agricultural systems in the face of evolving phytopathological challenges.