International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | IoT and Machine Learning Based Smart Hydroponics Monitoring System |
|---|---|
| ABSTRACT | Hydroponics is a soil-less cultivation technique that enables efficient crop production with minimal water usage and space requirements. However, maintaining optimal environmental and nutrient conditions requires continuous monitoring and timely control. Manual supervision is labor-intensive and prone to errors. This paper presents an IoT and Machine Learning based Smart Hydroponics Monitoring System that provides real-time sensing, automation, and intelligent condition prediction. An ESP32 microcontroller is interfaced with sensors to monitor pH, temperature, humidity, moisture, turbidity, and light intensity. Sensor data is transmitted to the ThingSpeak cloud platform for visualization and storage. Machine learning algorithms including Decision Tree, Naive Bayes, Support Vector Machine, and Random Forest are evaluated for system condition classification. Experimental results show that the Random Forest classifier achieves the highest accuracy of 99.9%. The proposed system improves reliability, reduces human intervention, and supports sustainable indoor farming. |
| AUTHOR | SRIDEVI N, DIMPLE N. SHETTY, KHUSHI S. GOWDA, HEMAVATHI K. HANGARKI Department of Electronics and Instrumentation Engineering, Dr. Ambedkar Institute of Technology, Bengaluru, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312095 |
| pdf/95_IoT and Machine Learning Based Smart Hydroponics Monitoring System.pdf | |
| KEYWORDS | |
| References | 1. A. Rahman et al., “Smart Hydroponics: AI and IoT Integrated System,” Springer, 2023. 2. B. Sharma, “IoT-Enabled Nutrient Management System,” 2022. 3. L. Breiman, “Random Forests,” Machine Learning, 2001. 4. Espressif Systems, “ESP32 Datasheet,” 2023. |
