Authors - Hiba GAIZI, Abderrahim BAJIT, Hamza BENZZINE, Youness ZAHID, Hicham ESSAMRI, Mohamed Nabil SFRIFI, Rachid EL BOUAYADI Abstract - The growing demand for sustainable agricultural practices is driven by urgent global environmental issues. Greenhouses provide controlled settings that improve plant productivity through technological innovations. A promising approach to addressing these challenges is the integration of smart greenhouses with mobile IoT nodes equipped with autonomous navigation capabilities. This article introduces an advanced mobile node that autonomously follows a predefined path in the greenhouse using sophisticated computer vision techniques and deep learning models. The mobile node utilizes convolutional neural networks (CNN) to precisely track the path and strategically pause at each plant, collecting comprehensive subjective and objective data, thus enhancing the conventional functionality of IoT nodes. Agricultural IoT devices play a pivotal role in data presentation and connectivity via wired and wireless synchronized communication systems, though data security continues to be a persistent issue. By leveraging computational intelligence, the system compensates for lost and inaccurate sensor data, producing critical forecasts through advanced data analysis tools, allowing farmers to make informed decisions and enhance overall performance. The mobile node not only gathers data but also operates as the master I2C controller[1], overseeing communication with various I2C slaves and ensuring efficient data exchange between the Cloud and IoT nodes. The methodologies described here enable the optimization of both objective and subjective PAYLOADs, significantly improving data analysis, predictive accuracy, energy efficiency, and overall system performance. This article outlines these techniques, highlighting their impact on reducing data transmission time and improving system effectiveness in smart greenhouse environments.
