Monitoring Crop Fields via Drone for Precision Agriculture

Abstract Unmanned Aerial Vehicles are becoming more and more popular to meet the demands of increased population and agriculture. Drones equipped with appropriate cameras, sensors and integrating modules will help in achieving efficient precision agriculture. The propose

Project Title

Monitoring Crop Fields via Drone for Precision Agriculture

Project Area of Specialization

Robotics

Project Summary

Abstract

Unmanned Aerial Vehicles are becoming more and more popular to meet the demands of increased population and agriculture. Drones equipped with appropriate cameras, sensors and integrating modules will help in achieving efficient precision agriculture. The proposed solutions related to these drones, if integrated with various Machine Learning and Internet of Things concepts, can help in increasing the scope of further improvement. Our Crop Monitoring Drone will include design decisions, a communication protocol, waypoint system and user interface while utilizing other software and hardware that helps with the control, detection and flow of the system.

This paper looks at the benefits of drones in agriculture, and their limitations, illustrating from examples how drones operate on farms. Different features of drones are discussed, specifically how our drone assists farmers in maximizing their harvest by detecting problems early, and managing the crops by using specific cameras to detect pests, water shortages and analyze growth of their crops.

Using modular approach, we are working on the autonomous parcel delivery concept using our drone which will be replaced with the delivery module and rest of the working will remain the same. The goal of our Autonomous Delivery Drone is to deliver parcel to our destined location. This will ensure time delivery, considering the fact that how often we get to interact with traffic jams in our cities throughout the country. Use of this Drone will bring advances to the transportation sector by providing efficient transport management as it cut downs the vehicle fuel cost and saves time of the human labor responsible for the parcel delivery. The idea is to use the technology in order to help humanity and a system like this can bring hope that not all tasks are futile.

Project Objectives

1. To collect relevant data for the training of our Machine Learning Model.
2. To train and deploy our Machine Learning Model using data we collected.
3. To use Machine Learning approach to sort data as required.
4. To select the hardware components of our drone.
5. To assemble hardware components that we selected to build our drone.
6. To ensure that the drone reach a destined waypoint/coordinate.
7. To acquire data from sensors and camera.
8. To capture picture or video of intended farm and send it to corresponding ground station.
9. To manage crop damage and growth assessment and emergence.

10. To ensure that the drone reach a destined waypoint/coordinate.

11. To enable the drone avoid any obstacle autonomously encountered during the flight.

12. To track an ArUco marker using Open-CV.

13. To perform precision landing on the ArUco marker.

14. To write mission scripts that enables drone to autonomously take off, land and maneuver around.

Project Implementation Method

Methodology

The methodology is to have a drone that takes coordinates in terms of longitude and latitude and moves to a destination way point after reaching target altitude. The coordinates can be reset by using by SSH via Laptop to the mission script. The drone adjusts its path using GPS. On reaching its destination, the sensors and cameras on drone will acquire data such as growth of vegetation, effective region on the fields or any other related defects and will send that data corresponding to the ground station for further processing and evaluation.

Benefits of the Project

Consumer for this product would comprise of courier service providers, medical stores and ambulance service providers, Agriculture Industry as it is a main source of income for our country. Our products prime focus would be assisting the farmers and stakeholders of this industry to estimate yield production. Also if we consider the parcel delivery then we would be able to send urgent supplies (especially medicines, blood and food supplies) to the remote areas.

Technical Details of Final Deliverable

Hardware Deliverable

We would be having a working autonomous drone which can fly to the provided destination coordinates and will return back with no pilot intervention. If working with agriculture then the drone would be collecting images of the farm (especially wheat) so our trained model can make predictions for the crop stage and yield production. 

If working with the parcel delivery then the drone would have a delivery module which can carry weight upto 0.5 kg and the rest of the working will remain same added that delivery module will be there. 

Software Deliverable:

A working model for the wheat crops which can predict the stages of wheat crop

Working simulations of the drone 

Final Deliverable of the Project

HW/SW integrated system

Core Industry

IT

Other Industries

Medical , Petroleum , Agriculture , Food , Manufacturing , Transportation , Security

Core Technology

Robotics

Other Technologies

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

Sustainable Development Goals

Decent Work and Economic Growth, Industry, Innovation and Infrastructure

Required Resources

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