The increasing need for sustainable agricultural techniques is driven by pressing global environmental challenges. Greenhouses offer controlled environments that enhance plant productivity through technological advancements. A promising solution to these challenges is the integration of smart greenhouses with mobile IoT nodes equipped with autonomous navigation capabilities. This article presents a sophisticated mobile node that autonomously traverses a predefined path in the greenhouse using advanced computer vision techniques and deep learning models. This mobile node employs convolutional neural networks (CNN) to accurately follow the path and strategically halt at each plant, gathering comprehensive subjective and objective data, thereby enhancing the traditional functionality of IoT nodes. Agricultural IoT devices play a critical role in data presentation and connectivity via wired and wireless synchronized communication networks, though data security remains a persistent challenge. This study underscores the importance of optimizing the PAYLOAD charge and incorporating cutting-edge technologies such as visual-based watermarking and real-time data synchronization. By harnessing computational intelligence, the system compensates for lost and inaccurate sensor data, generating essential forecasts through sophisticated data analysis tools, thus enabling farmers to make informed decisions and improve overall performance. The mobile node not only collects data but also functions as the master I2C controller, managing communication with various I2C slaves and ensuring efficient data exchange between the Cloud and IoT nodes. The described methodologies facilitate the optimization of both objective and subjective PAYLOADs, significantly enhancing data analysis, predictive accuracy, energy efficiency, and overall system performance. This article details these techniques, highlighting their impact on reducing data transportation time and boosting system efficacy in smart greenhouse environments.