Ways to Develop Environmentally Stress-Resilient Crops by AI-Powered Nanobiotechnology

The more frequent occurrences of environmental stresses such as drought, salinity, heat, and heavy metal toxicity have become increasingly severe challenges in crop yield and food security worldwide. Conventional farming systems can no longer meet these demands sustainably. This chapter discusses the convergence of AI and nanobiotechnology, an innovative strategy for synergistically promoting environmentally stress-tolerant crops. The unique physicochemical properties of ENMs, like metal-based, carbonaceous, and polymeric nanoparticles, have been utilized for modulation of antioxidant defense systems, osmolyte accumulation, hormonal regulation, and photosynthetic efficiency in plants. In combination with AI-based predictive modeling, ML algorithms will be able to optimize nanoparticle synthesis, delivery, and their environmental safety on one side, and nutrient use efficiency on the other end, thereby decreasing toxicity accordingly. Precision agriculture through nanosensor networks, remote sensing, and data-driven irrigation and pest control systems. In addition, the combination of nanocarrier-based gene editing with an AI-supported genomic selection expedites stress-resistant breeding. Although several challenges like nanotoxicity, data standardization, and economic scalability remain to be addressed, this interdisciplinary paradigm is in harmony with the United Nations Sustainable Development Goals (SDGs), which will provide a way forward to resilient agricultural ecosystems and global food security.