An Integrated System for Real-Time Greenhouse Environmental Forecasting and Adaptive Crop Management

This paper presents the Greenhouse Intelligence System, an advanced agricultural platform that integrates NASA Earth observation data with artificial intelligence analytics to optimize greenhouse management. The system employs a multi-agent architecture combining real-time environmental monitoring, predictive modeling, and natural language processing to deliver actionable recommendations for crop development. Temperature prediction utilizes a Gated Recurrent Unit neural network that achieves 95% accuracy in forecasting environmental conditions, significantly outperforming traditional monitoring methods which achieve only 75% accuracy. The implemented system integrates NASA POWER API weather data with on-premises metrics to evaluate crop suitability for lettuce, tomato, bell pepper, cucumber, and spinach. An intelligent recommendation engine ensures optimal growth conditions through adaptive actuator control logic, achieving resource conservation of up to 40% for pesticides, 35% for water, 30% for fertilizer, and 25% for energy. The user interface provides real-time environmental analysis through interactive maps and data visualization. Expert agents for monitoring, forecasting, and memory management collaborate to deliver context-aware suggestions. A retrieval-augmented generation architecture supports natural language interaction, providing precise responses to user queries. The modular architecture ensures robustness and adaptability across diverse greenhouse environments, advancing intelligent agricultural systems with enhanced resource efficiency and increased crop production.