ARTIFICIAL INTELLIGENCE TECHNIQUES FOR QUANTUM-ENHANCED NANOSENSOR DEVELOPMENT IN PRECISION AGRICULTURE AND REAL-TIME CROP MONITORING

Precision agriculture is essential for addressing food insecurity and practicing sustainable agriculture. Traditional precision agricultural methods sometimes have issues with slow data analysis and inadequate sensitivity to detect minute changes in the environment, which can result in missed treatments and wasted money. These obstacles make it more difficult to anticipate crop health problems or environmental stresses. A new method of data-driven decision-making for realtime crop monitoring has been practicable by combining artificial intelligence with quantumenhanced nanosensors. The paper proposes a novel method, Quantum-Enhanced Nanosensor development using Reinforcement Learning (QENDRL), to manage agricultural resources and improve crop productivity dynamically. The QENDRL method combines a Deep Q-Learning framework with quantum-enhanced nanosensors to allow for real-time monitoring of soil moisture, nutrient levels, and environmental factors. The long-term goal of the Reinforcement Learning (RL) agent is to learn the optimal strategies for watering, fertilizing, and controlling pests to optimize crop yield with minimal resource waste. The system dynamically adapts to changing field conditions by utilizing continual sensor feedback. According to the primary findings, QENDRL improves data accuracy and precision by 25% compared to the previous methods and resource efficiency by 30%. Early detection and adaptive interventions also increased crop output by 25% in field trials. The research concluded that QENDRL is an excellent method for integrating AI with quantum-enhanced nanosensors, which has the potential to boost the efficiency and longevity of precision farming substantially