TY - GEN
T1 - Smart NFT based Hydroponic System for Precision Agriculture using IoT and AI
AU - Chetan, R.
AU - Asha, C. S.
AU - Raghavendra Rao, P.
AU - Suresh, Shilpa
AU - Guruprasad, null
AU - Kumar, Dhanush
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Precision agriculture (PA) presents numerous possibilities to address the growing challenges of sustainability and food security. To develop an effective PA system, it is essential to collect and analyze real-time data using Artificial Intelligence (AI) methodologies. This research integrates the Internet of Things (IoT) and AI within a Nutrient Film Technique (NFT) hydroponic system for the growth of the Holy basil plant. The aim is to create an ideal indoor environment through optimized resource management. This involves establishing an IoT-enabled framework for real-time data collection to monitor pH, temperature, humidity, and dissolved substances, affecting both artificial conditions and plant growth rates. The AI processes these data to fine-tune the environment and perform the necessary automated adjustments within the PA system. In addition, AI-driven predictive models will anticipate plant growth patterns and ensure a robust crop management system with precise yield predictions. Addressing these areas is crucial for the successful application of IoT and AI in PA systems. The Random Forest machine learning (ML) model predicts the yield in cm with an accuracy of 97%, while XGBoost is used to predict the NPK dosing with an accuracy of 99%.
AB - Precision agriculture (PA) presents numerous possibilities to address the growing challenges of sustainability and food security. To develop an effective PA system, it is essential to collect and analyze real-time data using Artificial Intelligence (AI) methodologies. This research integrates the Internet of Things (IoT) and AI within a Nutrient Film Technique (NFT) hydroponic system for the growth of the Holy basil plant. The aim is to create an ideal indoor environment through optimized resource management. This involves establishing an IoT-enabled framework for real-time data collection to monitor pH, temperature, humidity, and dissolved substances, affecting both artificial conditions and plant growth rates. The AI processes these data to fine-tune the environment and perform the necessary automated adjustments within the PA system. In addition, AI-driven predictive models will anticipate plant growth patterns and ensure a robust crop management system with precise yield predictions. Addressing these areas is crucial for the successful application of IoT and AI in PA systems. The Random Forest machine learning (ML) model predicts the yield in cm with an accuracy of 97%, while XGBoost is used to predict the NPK dosing with an accuracy of 99%.
UR - https://www.scopus.com/pages/publications/105012712872
UR - https://www.scopus.com/pages/publications/105012712872#tab=citedBy
U2 - 10.1109/AMATHE65477.2025.11081252
DO - 10.1109/AMATHE65477.2025.11081252
M3 - Conference contribution
AN - SCOPUS:105012712872
T3 - 2025 IEEE 2nd International Conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2025 - Proceedings
BT - 2025 IEEE 2nd International Conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2025 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2025
Y2 - 24 April 2025 through 25 April 2025
ER -
