Urban Agriculture through IoT-based Resilient Hydroponic Farming – A Machine Learning Approach - Pattnaik, Sushant Kumar; Samal, Soumya Ranjan; Bandopadhaya, Shuvabrata; Pradhan, Debi Prasad; Das, Jitendra Kumar; Ivanov, Antoni; Poulkov, Vladmir; Mihovska, Albena (Journal of Mobile Multimedia, 2025)
Procuring resilience, resource efficiency, productivity, pest and malady control in agrarian production is imperative when climate change poses a threat. In recent years, hydroponics is considered as an emerging farming technique and is popular in urban areas due to its minimal water use and ability to grow plants without soil. In the Nutrient Film Technique (NFT) based hydroponics system, plants are cultivated by using water content nutrient solutions. Integration of Internet of Things (IoT) technology to NFT based hydroponic systems, many advancements such as minimizing water usage, real-time plant growth monitoring, efficient nutrient diffusion and reduction in human efforts can be achieved. In this work, an IoT based smart hydroponics system using NFT is proposed. Key components of the proposed solution include sensor networks for data acquisition, a robust Machine Learning (ML) framework for data analysis and prediction, as well as actuators for automated control of environmental conditions. The system monitors different real-time environmental parameters and the status of the plant’s growth and controls the nutritional value of water in an automated and cost-effective way. A Support Vector Machine (SVM) algorithm is used to predict the pH values with an accuracy of 89.6%, surpassing the Decision Tree (DT) and Random Forest Regression methods.