Agriculture contributes significantly to the economy and is predicated on quantifiable agricultural output, which is heavily reliant on irrigation. Due to improper management, lack of facilities and sources, the farmer takes a lot of time, hard work and also the water gets wasted. This demands for the automatic system which can deliver an effective solution. In this autonomous system, a soil moisture sensor is installed for measuring the moisture level in soil, there is also a water level sensor that ensure the water quantity is equal from main water channel to water channel that is connected to crop field. All sensors are controlled with Arduino. Arduino send values to and from the Raspberry. Farmers can check weather by putting the location of field in app then by analyzing the moisture sensor, weather forecast and geographical data of soil, feature app will notify the farmers about required quantity of water in crops. There will be water channel connected to all field of crops and MCU nodes at the end where fields need water. This will be control by Raspberry via wireless LAN.

Objective
This machine will work so efficiently and water the fields accurately. There will no wastage of water by using this machine. This project will really be helpful in those specific areas having limited quantity of water. Every area has different type of soil. The amount of water will depend on type of soil. Android app in this project will also involve the feature of weather forecast that will inform farmers about geographically history of area and future weather either there be a rainfall or not. This app will be linked with agriculture department. So, farmer will get right tips regarding right time for crops. In case of crop epidemic this app will help by letting farmers know about fertilizer and spray required at that time.

https://www.eajournals.org/wp-content/uploads/Automation-of-Irrigation-Systems-and-Design-of-Automated-Irrigation-Systems.pdf

• Asia and Pacific

• Remote and rural communities

Data-Driven Approach: Machine learning relies heavily on data. As the system collects data from various sensors (soil moisture, weather conditions, crop health), it continuously improves its algorithms and decision-making capabilities.

Flexibility and Adaptability: Machine learning models can be trained and fine-tuned to suit specific crops and local environmental conditions. This adaptability enables the system to be replicated across diverse agricultural settings, from arid regions to humid climates, and across various crop types.

Scalable IoT Infrastructure: IoT technology allows for seamless integration of sensors and actuators. Deploying these sensors in a scalable manner is relatively straightforward. As long as there is internet connectivity, the system can be extended and replicated in different regions, rural or urban.

Cloud-Based Solutions: Cloud computing provides a scalable and cost-effective approach to manage data and run machine learning algorithms. Cloud platforms enable easy access to the system from different locations, facilitating replication without significant hardware investments.

Localized Optimization: While the overall system design and architecture can be replicated, local optimization is essential. Factors like soil composition, weather patterns, and crop preferences may vary between regions.

Partnerships and Collaboration: Collaborating with local governments, agricultural organizations, and universities can aid in understanding specific regional challenges and constraints.

Training and Support: Ensuring that local farmers and technicians receive adequate training and support is crucial. Building capacity among local stakeholders helps them embrace and adapt the technology effectively.

Economic Viability: The scalability and replicability of the system depend on its economic feasibility for farmers. Cost-benefit analyses and economic models can be employed to demonstrate the value of adopting the technology in different contexts.

The project promotes sustainability by addressing the efficient use of water resources in agriculture, a critical aspect of environmental sustainability. By utilizing soil moisture sensors, water level sensors, and weather forecasting, the system optimizes irrigation practices, reducing water wastage and promoting economic and environmental sustainability in agriculture. Additionally, the mobile app's integration with agricultural departments ensures continuous support and updates for farmers, contributing to the long-term viability of the project.

This project directly relates to the WSIS Forum's focus on ICT applications in E-agriculture. It demonstrates the use of technology to address agricultural challenges and promote efficient resource management.