Smart Farming Through Internet of Agriculture Things and Robots

The purpose of this project is to develop a data driven smart farming platform using Internet of Internet of Agricultural Things (IoAT) devices and sensors mounted on mobile robots. It will provide systematic storage and processing using big data tools, and treatment of data near the source using fo

Project Title

Smart Farming Through Internet of Agriculture Things and Robots

Project Area of Specialization

Internet of Things

Project Summary

The purpose of this project is to develop a data driven smart farming platform using Internet of Internet of Agricultural Things (IoAT) devices and sensors mounted on mobile robots. It will provide systematic storage and processing using big data tools, and treatment of data near the source using fogs/edge devices Crops and soil health will be monitored using static and robot based IoAT devices. This data will be transferred to the server using LoRA and Wifi. On server side the data prepared, and cleansed o make them suitable for the application of Neural network and other Machine learning algorithms.  Finally, at presentation layer, for applications and data display, which enables querying databases. The data will be displayed in an android application. The application will take feedback form the user and high level control commands for the robots and IoT devices form the user.

Project Objectives

1. To design and develop automatic data acquisition system using static sensors, as well as cooperative mobile robots equipped with IoAT sensors;
2.  To develop data pre-processing, data reduction in order to make data valuable and to only transfer useful data;
3.  To design the system to handle and store data streams and historical data efficient database;
4.  To design machine learning-based algorithms to monitor crop health;
5.  To design android-based interface to provide visualization of real-time data and data analysis;
6.  To develop control algorithms for ground robots and IoAT sensors;

Project Implementation Method

1. Requirement Engineering:  Functional and non-functional requirements will be outline and will be presented and communicated using use case UML diagram.  

2. Analysis: Detailed analysis will be done using software engineering principles for all the modules. Detailed Data model, ERD and other UML diagram will be developed for each module.

3. Design: Both hardware and software module will be designed respective engineering principles. Based on our preliminary study following module are expected to be designed.

 a. Data collection: Data collection module includes Static IoT node and mobile robotic platforms equipped with advanced IoAT sensors, including camera, moisture, temperature etc.

b. Contextualization, Data Reduction and Transmission module: It will append the contextual information to the sensor data and delete redundant and noisy data. It will also transfer data from sensor to the Fog and Cloud.

c.  Data Pre-Processing: Quality of data is a critical factor in the success of data analytics and decision making. The data collected by moving and static sensors may contain discrepancies and inconsistencies.

d. Data Management: It will provide data storage at the fogs, raw data in data lakes, and processed/curated data in a warehouse

e. Real-time Data Analytics: Real-time (on the fogs) and off-line (on the cloud) data analytics to improve HTP are performed and made possible through the powerful fusion of “big data” and “IoAT technologies”.

f. Visualization: The visualization of the output is very important because the famers and other stakeholder must be able to understand complex analytics of agriculture big data.

4. Testing: The module will be tested using comprehensive Test plan which includes unit and integration testing.

5. Documentation and dissemination:  All the modules will be documented in light of software engineering techniques. The project progress will be shared at university and national level.

Benefits of the Project

1. To improving crop yield through the efficient data driven IoAT and Robotics technologies;
2. It may sustain the economic viability of farm operations;
3. satisfy and improve human food and fiber needs;
4. it may improve in help in better crop management
5. helps in decisions to be made when, where and what to cultivate

Technical Details of Final Deliverable

1. Data acquisition system which includes two ground robots equipped with IoAT sensors and static IoAT nodes. The main sensor we plan to use in camera, though we will acquire data form other sensors as well.
2. Communication Module: Mechanism for Data Transfer suing Wifi or LoRA. It will be implemented using Raspberry-pi
3. Data Analytics: Data Analytics Module using Neural Network. (it includes small dataset as well). We plan to use Tensorflow for the data analytics.
4. Visualization: Aneroid application to visualize sensors data and data analysis.

Final Deliverable of the Project

Hardware System

Type of Industry

IT , Agriculture , Food

Technologies

Artificial Intelligence(AI), Internet of Things (IoT)

Sustainable Development Goals

No Poverty, Zero Hunger, Good Health and Well-Being for People, Life on Land

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com