Smart plant surveillance is a crucial aspect of modern precision agriculture. This paper presents an IoT- and AI-enabled irrigation monitoring system that utilizes Blynk IoT and NodeMCU. Designed for low power consumption, the system can operate via a mini-UPS during power outages and auto-reconnects once power is restored.

Efficient water management is a major concern in agriculture. This proposed system addresses it through a wireless sensor network that performs real-time in-field sensing and variable rate irrigation. The system integrates multiple sensors including soil moisture and DHT11 (temperature and humidity), managed by a NodeMCU microcontroller. Sensor data is transmitted to the cloud and accessed via a mobile application, allowing farmers to monitor field conditions remotely and interact with the system using their unique login credentials.

Artificial Intelligence (AI) is employed for data analysis and predictive decision-making. Based on historical and real-time data trends, the system can optimize irrigation schedules, detect anomalies, and notify the farmer about potential crop health issues. The AI module can be extended to support crop-specific irrigation patterns for precision farming.

The system's software is developed using the Arduino IDE in embedded C, while the user interface is built on Android using Blynk. The combination of real-time sensing, AI analytics, and cloud-based monitoring provides a fully automated and intelligent irrigation system, significantly reducing manual labor and improving agricultural productivity. This scalable model adapts to different farm sizes by adding more sensors, ensuring uniform irrigation and efficient water usage across all zones.

    Features:  

• Using IOT technology for the communicating the system and farmers.
• The wastage of excess water in system traditional has been triumph over by using proposed system
• Highly sensitive.
• Power saving.

    The main blocks of this project are:  

1. Regulated power supply.
2. Microcontroller (NodeMCU).
3. Motor with driver.
4. Soil moisture sensor.
5. DHT11.
6. LED.

    Software’s used:  

1. Node MCU compiling and dumping code into Microcontroller
2. Express SCH for Circuit design.

    Regulated Power Supply:

                   

video: