PetalGrid: An AI–IoT Based Smart Garden Monitoring and Automated Irrigation System

Abstract

Modern horticulture and botanical garden management require continuous monitoring of environmental conditions to maintain plant health and optimize resource usage. Traditional irrigation and monitoring methods rely heavily on manual observation, which often leads to inefficient water usage and delayed responses to environmental changes. This paper presents PetalGrid, an Artificial Intelligence (AI) and Internet of Things (IoT) based smart garden monitoring system designed to automate plant care using real-time environmental sensing and predictive analytics. The system integrates multiple sensors including soil moisture, temperature, humidity, and light sensors connected to a NodeMCU microcontroller. Sensor data is transmitted through Wi?Fi to a cloud-based dashboard where analytics and monitoring are performed. Machine learning techniques are utilized to analyze environmental patterns and predict irrigation requirements. Based on these predictions, the system can automatically control actuators such as water pumps and lighting systems. Experimental implementation demonstrates that PetalGrid enables efficient water management, reduces manual intervention, and improves plant health through intelligent automation. The system is scalable, secure, and suitable for applications ranging from small indoor gardens to large botanical environments.

Introduction

This research introduces PetalGrid, an intelligent IoT- and AI-based smart garden monitoring and irrigation system designed to improve water efficiency and plant health. Traditional irrigation systems operate on fixed schedules, often causing overwatering or underwatering. With increasing water scarcity and the need for sustainable resource management, automated and data-driven solutions are essential.

PetalGrid uses real-time environmental sensing combined with machine learning to monitor key parameters such as soil moisture, temperature, humidity, and light intensity. The system collects data through distributed sensors connected to a NodeMCU (ESP8266) microcontroller, which transmits information to a cloud platform for storage and analysis. AI-based predictive models analyze environmental trends to determine optimal irrigation timing and automatically activate watering when needed.

The system includes a cloud-based dashboard that allows users to monitor conditions, receive alerts, and manually control irrigation if required. Its architecture consists of three layers:

1. Sensing Layer – Environmental data collection using sensors.

2. Processing Layer – Cloud communication and AI-based predictive analysis.

3. Application Layer – User interface for real-time monitoring and control.

The key contributions of the work include:

• Development of an IoT-based garden monitoring network

• Integration of AI-driven predictive irrigation

• Automated irrigation control

• Real-time cloud monitoring

• Scalable and secure system architecture

Overall, PetalGrid provides a smart, scalable, and efficient solution for modern agricultural and horticultural environments, improving water conservation and environmental management.

Conclusion

This paper presented PetalGrid, an AI and IoT based smart garden monitoring system designed to improve plant management through intelligent automation. The system integrates environmental sensors, wireless communication, cloud-based analytics, and automated irrigation mechanisms to maintain optimal plant conditions. By combining real-time monitoring with predictive analytics, the system reduces water wastage and minimizes human intervention. The modular design allows the system to scale easily for larger garden environments or agricultural applications. Future improvements may include integrating advanced machine learning models, mobile application support, and additional environmental sensors for more precise monitoring. PetalGrid demonstrates the potential of combining AI and IoT technologies to create sustainable and intelligent horticultural systems.

References

[1] J. Smith, “IoT-Based Smart Irrigation Systems,” IEEE Journal of Smart Agriculture, 2021. [2] K. Brown, “Internet of Things in Precision Agriculture,” Springer, 2020. [3] R. Gupta and P. Sharma, “Machine Learning Applications in Smart Farming,” Elsevier, 2022. [4] M. Patel, “Wireless Sensor Networks for Environmental Monitoring,” International Journal of IoT Research, 2021. [5] A. Kumar, “Automated Irrigation Systems Using IoT,” International Journal of Agricultural Technology, 2020. [6] S. Verma, “AI-Based Predictive Models for Smart Agriculture,” IEEE Conference on Smart Systems, 2022.

Copyright

Copyright © 2026 Prajwalankita Balagoni, A. Sravanthi, R. Manish Kumar, N. Vikas Sharma, Mr. V. Veeresh. 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.