An Energy-Efficient All-Dynamic Multiparameter Sensor for Battery-Less Smart Nodes in Agricultural Internet of Things (AIoT) with Machine Learning-Based Soil Moisture and Weather Prediction The rapid growth of precision agriculture demands intelligent monitoring systems that can optimize water usage, improve crop productivity, and reduce energy consumption. This project presents an Energy-Efficient All-Dynamic Multiparameter Sensor System for Battery-Less Smart Nodes in Agricultural Internet of Things (AIoT) using a Raspberry Pi Zero 2W as the central processing unit. The system continuously monitors important environmental parameters such as soil moisture, temperature, humidity, rainfall, and light intensity using a Capacitive Soil Moisture Sensor, DHT22 Sensor, Rain Sensor, and LDR Module. The status of the project will display on LCD. Here Arduino NANO  works as a ADC converter. The collected sensor data is transmitted to a web-based monitoring platform, allowing farmers to remotely observe real-time field conditions from anywhere through the internet. To enhance decision-making, the system incorporates Machine Learning algorithms that analyze past and present sensor data to predict future soil moisture levels and weather conditions. Based on these predictions, the system intelligently determines the irrigation requirements of the field. An automatic irrigation control mechanism is implemented using a relay-driven AC water motor. When the predicted soil moisture level falls below a predefined threshold and no rainfall is expected, the system automatically activates the water motor. Similarly, the motor is turned OFF when sufficient soil moisture is achieved or rainfall is predicted, thereby preventing water wastage and improving irrigation efficiency. The proposed battery-less and energy-efficient architecture minimizes maintenance requirements while ensuring continuous operation. By integrating IoT, Machine Learning, cloud monitoring, and automated irrigation control, the system provides a smart, sustainable, and cost-effective solution for modern agriculture, helping farmers conserve water, reduce labor, and maximize crop yield.        

Objectives:  

1. To monitor soil moisture, temperature, humidity, rainfall, and light intensity using sensors.
2. To display sensor data on a web page for remote monitoring.
3. To display sensor data on LCD for local monitoring.
4. To store sensor data for future analysis.
5. To predict future soil moisture levels using Machine Learning.
6. To forecast weather conditions based on collected sensor data.
7. To automatically control the AC water motor using a relay.
8. To reduce water wastage through smart irrigation.
9. To improve crop growth and agricultural productivity.
10. To minimize human effort in irrigation management.
11. To develop an energy-efficient IoT-based smart farming system.

          The major building blocks of the project are:

• Regulated Power Supply.
• Raspberry pi zero 2W.
• Arduino NANO.
• SD card.
• LDR sensor.
• DHT22 sensor.
• Capacitive Soil sensor.
• Rain sensor.
• Relay with AC motor.
• LCD display.

            Software’s used:  

1. Raspbian OS.
2. Machine Learning.
3. Express SCH for Circuit design.

        Block diagram:      

video: