AI-Enabled Nano Biosensors for Estimating Heavy Metal Contamination in Crops

Abstract

Heavy metal pollution poses a serious global threat to food safety and sustainable agriculture. Routine evaluation of harmful substances such as cadmium (Cd), lead (Pb), mercury (Hg), and arsenic (As) in crops is essential to prevent their accumulation in the food supply. Conventional detection techniques, including atomic absorption spectrometry and ICP-MS, are often expensive, labor-intensive, and unsuitable for field applications. In this context, merging artificial intelligence (AI) with nano biosensors introduces an innovative approach for rapid and precise detection. Nano biosensors, which utilize nanomaterials alongside biorecognition elements, provide remarkable sensitivity and specificity for identifying various heavy metals, even at minimal concentrations. When combined with AI and machine learning, these sensors allow for instant data processing, predictive analysis, and spatial mapping of contaminated sites. These real-time observations empower farmers and environmental organizations to make time. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.