Real‐Time Phosphate Monitoring via Plant‐Derived Graphene Ink FET Sensors Integrated with Deep Learning

Real‐time monitoring of plant nutrient levels, particularly phosphate, is essential for optimizing plant growth and addressing nutrient imbalances in precision agriculture. Conventional sensors mostly suffer from poor stability, reproducibility, matrix effects, and high costs, limiting their scalability and practical application. To overcome these challenges, a deep learning‐integrated remote‐gate field‐effect transistor sensor utilizing a plant‐derived graphene electrode is introduced for enhanced performance and reliability. These solution‐processed graphene electrodes, composed of cellulose nanocrystals from plant fibers, are functionalized with phosphate‐capturing ferritin and serve as the sensing surface, capacitively coupled to a commercial n‐type field‐effect transistor to address device variability issues. Deep learning integration significantly improved accuracy, enabling robust and precise phosphate detection. The sensor demonstrates a sensitivity of 14.1 mV dec −1 after the pH correction, a coefficient of variation of responses below 5%, and a 1 ng mL −1 (1 ppb) detection limit. As a proof‐of‐concept, phosphate levels in Hoagland solution, a standard plant nutrient medium, were monitored, achieving an r 2 of 0.951 and a coefficient of variation of 5.39%. A handheld prototype system further demonstrates its potential for on‐site continuous monitoring. This sustainable and cost‐effective approach provides a scalable solution for real‐time phosphate detection with high sensitivity and reproducibility, meeting agricultural demands.