IoT Enabled Plant Growth and Health Monitoring and Prediction

Abstract

The rapid advancement of the Internet of Things (IoT) has significantly transformed traditional methods of environmental monitoring by enabling intelligent, automated, and real-time data acquisition systems. In agriculture and plant care, continuous monitoring of temperature, humidity, and soil moisture is essential for ensuring optimal plant health. This paper presents a cost-effective IoT-based plant growth and health monitoring system using the NodeMCU ESP8266 platform, integrated with a DHT11 and soil moisture sensor. A machine learning model further classifies plant leaf conditions into four health categories: Healthy, Rust, Slug damage, and Powdery Mildew. Sensor data is processed and served through an embedded web server, enabling remote real-time monitoring via any standard web browser. Experimental results validate system accuracy, reliability, and suitability for smart agriculture applications.

Introduction

The text presents an IoT-based smart plant monitoring system that uses the NodeMCU ESP8266, sensors, and machine learning to improve agricultural and plant care practices through real-time monitoring and automated disease detection.

Traditional plant monitoring methods rely on manual observation, which is inefficient, error-prone, and lacks real-time data. To overcome this, the proposed system continuously measures environmental conditions such as temperature, humidity (using the DHT11 sensor), and soil moisture, while also using a CNN-based machine learning model to classify plant leaf diseases (Healthy, Rust, Slug damage, Powdery Mildew).

The system is built on a three-layer architecture: the sensing layer collects environmental data, the processing layer (NodeMCU) handles data acquisition and hosts a web server, and the application layer provides a browser-based interface for real-time monitoring and disease prediction. Users can remotely access live plant conditions through any web browser without additional software.

The hardware setup includes sensors, LEDs, a buzzer, and the NodeMCU, while the software uses Arduino IDE for programming and Python-based deep learning tools (TensorFlow/Keras) for training the disease classification model. The system is low-cost, scalable, and designed for small-scale agriculture.

The machine learning component uses transfer learning to improve accuracy in classifying leaf diseases from images. The system automatically processes sensor data, displays it in real time via a web interface, and provides early detection of plant health issues.

Conclusion

The IoT-enabled plant growth and health monitoring system demonstrates a cost-effective, real-time, and user- friendly solution for smart agriculture and greenhouse applications. The system integrates NodeMCU ESP8266, DHT11, and soil moisture sensors with an ML-based plant disease classifier, enabling automated identification of Healthy, Rust, Slug damage, and Powdery Mildew conditions. Hardware prototypes (Figs. 2–3) and experimental results confirm reliability, accuracy, and ease of deployment. Future enhancements include: 1) Automated irrigation control based on soil moisture thresholds 2) Expanded ML model for additional disease categories and crops 3) Mobile application with push notifications and alert management 4) Cloud storage for long-term trend analysis and visualization 5) Solar-powered modules for remote autonomous deployments 6) Multi-node deployment for large-scale smart greenhouse monitoring

References

[1] Bahga and V. Madisetti, Internet of Things: A Hands-On Approach, 2nd ed. Universities Press, 2019. [2] S. Monk, Programming the ESP8266: Getting Started with Arduino IDE. McGraw-Hill Education, 2017. [3] G. S. Kishore and K. K. Reddy, \"IoT-Based Plant Monitoring System for Smart Agriculture,\" Int. J. Adv. Res. Comput. Sci., vol. 10, no. 3, 2019. [4] P. K. Gupta and S. Sharma, \"Wireless Sensor Network for Soil Moisture and Environmental Monitoring,\" J. Ambient Intell. Humanized Comput., 2020. [5] R. A. Patel and M. K. Mehta, \"Smart Greenhouse Using IoT and Wireless Sensor Networks,\" Int. J. Comput. Appl., vol. 182, no. 41, 2021. [6] Espressif Systems, ESP8266 Technical Reference. [Online]. Available: https://www.espressif.com/en/products/socs/esp8266 [7] Arduino IDE Documentation. [Online]. Available: https://www.arduino.cc/en/software [8] IEEE Standard for IoT Devices and Sensor Networks, IEEE Std 2413-2019. [9] K. He, X. Zhang, S. Ren, and J. Sun, \"Deep Residual Learning for Image Recognition,\" in Proc. IEEE CVPR, 2016, pp. 770– 778.

Copyright

Copyright © 2026 K. Balu Siva Sri Kumar, J. Balu, V. Sravani, A. Sunitha, V. Abhishek , P.V.S. Anusha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.