10th International Congress on Information and Communication Technology

Authors - Youness ZAHID, Abderrahim BAJIT, Hiba GAIZI, Hicham ESSAMRI, Rachid EL BOUAYADI
Abstract - The increasing need for sustainable agricultural practices stems from urgent global environmental concerns. Greenhouses offer controlled environments that enhance crop productivity by leveraging technological advancements. A promising solution to these challenges involves integrating smart greenhouses with mobile IoT nodes equipped with autonomous navigation capabilities. This paper presents an advanced mobile node capable of autonomously navigating a predefined path in the greenhouse, utilizing advanced computer vision and deep learning models. The node employs convolutional neural networks (CNNs) to accurately follow the path and pause at each plant, gathering comprehensive objective and subjective data, thereby enhancing traditional IoT functionalities. These IoT devices are critical for presenting data and establishing communication via synchronized wired and wireless systems. However, data security remains a persistent challenge. Through computational intelligence, the system compensates for lost or erroneous sensor data, delivering precise forecasts and enabling farmers to make data-driven decisions to enhance productivity. The mobile node not only collects data but also functions as an IoT hub, managing communication with various sensors and ensuring effective data exchange between IoT nodes and the cloud. This article outlines methods that optimize data analysis, predictive accuracy, and energy efficiency, significantly improving system performance. Our approach offers a solution that addresses sustainability while enhancing operational efficiency in smart greenhouses.