Neuro-Responsive Farming: Integrating Plant Electrophysiology for Real-Time Crop Decision-Making Systems

Abstract

Neuro-Answer Cultivation (NRF) introduces an innovative paradigm in accurate agriculture using the internal electrical signals of plants to direct real-time agricultural decisions. Recent discoveries in the plant electrophysiology have shown that plants produce complex bioelectrical patterns in response to environmental signals - functioning of nerve reactions in animals. This study proposes a comprehensive outline that integrates the plant electrophysiological sensor, artificial intelligence and automated control systems to create adaptive response loops for crop management. By explaining voltage changes and electrical chemical signals within plants, NRF systems can autonomize irrigation, nutrient distribution and insect mitigation strategies autonomously adapted. Experimental beliefs show that such systems increase crop yield, conserve resources, and improve plants stress tolerance. It also evaluates technical and biological challenges in paper signal acquisition, noise filtration and system scalability, while shedding the future of sustainable farming highlights the inter -disciplinary coordination of biology, information and agricultural engineering. Traditional farming system often depends on indirect environmental measurements, which themselves look at the congenital signaling capacity of plants. Neuro-Answer Cultivation (NRF) plant shows a paradigm change by taking advantage of electrophysiology-as a real-time reaction mechanism for natural electrical signals that arise in response to environment and physical stimuli. This study suggests how decoding these bioelectrical signals enables crops to directly inform the exact farming systems. By integrating the biocompatible sensor, wireless data acquisition and machine learning algorithms, we establish a structure that combines plant reactions to automated decision -making processes, adapting the delivery of water and nutrients depending on the needs of the real -time plant. The research plant highlights the interrelationship of physiological processes-such as movement, development and signal transmission-and validate the NRF model in both controlled and semi-region situations.