Smart Hydroponic Greenhouse Using Business Intelligence Tools and Deep Learning Algorithms

Hydroponics is a soilless farming technique in which the plants are irrigated with a nutrient solution consisting of water and compounds necessary to provide all the essential elements for normal mineral nutrition. Increase in population, industrialization which has lead to pollution and change in climatic condition has pose a serious threat to food security. This paper therefore explores the integration of Deep Learning (DL) and Business Intelligence (BI) in smart hydroponic greenhouse systems, aiming to optimize cultivation through data-driven automation. A conceptual architecture is presented, highlighting the flow of information from sensor inputs and cameras, through a Raspberry Pi and IoT gateway, to a central database. ANNs, including classification and prediction models, process this data, enabling automated control of actuators and providing actionable insights through a BI dashboard. The discussion of findings, based on reviewed literature and the proposed architecture, reveals a strong trend towards leveraging advanced technologies for improved efficiency, accuracy, and productivity in hydroponic agriculture. The integration of deep learning for tasks like disease detection and yield prediction, coupled with BI for data visualization and decision support, underscores the potential of these technologies to revolutionize hydroponic practices. This research emphasizes the importance of data-driven approaches, IoT infrastructure, and closed-loop control systems in creating intelligent and sustainable greenhouse environments.