The increasing complexity of greenhouse agriculture requires integrated, scalable platforms capable of precise monitoring, real-time decision-making, and sustainable management. This work presents an intelligent greenhouse system that transitions from static IoT networks to a multi-agent architecture combining mobile sensing units, autonomous robots, and deep learning-powered computer vision. Environmental parameters such as temperature, humidity, and soil moisture are continuously collected through embedded sensors, while high-resolution visual data enables detailed plant health assessment. Machine learning models analyze these multimodal datasets to predict crop conditions, detect anomalies, and enable early intervention. A tri-modal vision system ensures complete coverage: static wide-angle cameras for global monitoring, mobile units with CNN-based analysis for close-range inspections, and robot-mounted cameras for real-time tasks. Autonomous robots, equipped with SLAM and path planning algorithms, perform precision operations such as targeted harvesting and disease removal. The fusion of robotics, AI, and sensor networks provides high-resolution monitoring, optimizes resource usage, and enhances crop management. Additionally, onboard edge computing enables fast, localized data processing, reducing latency and improving system autonomy.