High-Level Project Summary
Drone technology is helping us increase ecosystems resilience in the face of a changing climate. This kind of technology will allow both urban and rural communities to be better prepared for climate change and drought, and water managers plan accordingly to reduce impacts on people and nature. In this context, we have developed drone technology in our project to prevent unnecessary and excessive irrigation in agricultural land will reveal whether the plant needs water and, if so, what amount of water should be given. The results obtained at the end of the project will provide the farmers with a roadmap for correct irrigation. Thus, it is foreseen that the farmer will use water correctly .
Link to Final Project
Link to Project "Demo"
Detailed Project Description
In our project, we used RGB sensor in drone design. This sensor captures red, green and blue light and is the most common and largely available commercial cameras. we chose this camera because of its potential and low cost operating requirements. Green/Red Vegetation Index (GRVI), Greenness Index (GI) and Extreme Greenness Index (EGI) are calculated using these sensors (ExG) with acceptable or high levels of accuracy. We created our drone in Tinkercad. The view of our drone from different angles is given in figure 1.
Figure 1. Our designed Drone
By uploading this sensor data to one of the available mapping and data analysis platforms, we can convert various multispectral data bands into indices of plant health and stress. Indices require varying amounts of spectral data.
Farmers have more requirements for planting completion. Remote sensing is a major technology to reduce this requirement. Global positioning system (GPS) with drone will be used in our project.
We will use Light Detection and Range (LiDAR) sensors to scan the ground and measure varying distances. The results obtained from this will allow them to be used to produce high-resolution maps and 3D models of natural and man-made objects. With this application in agriculture, elevation maps can be created to identify areas requiring improved drainage, drought stress can be monitored at different growth stages and 3D crop models can be created to optimize water use.
Before leaving the area, data collected by special software created by the sensor manufacturer will be previewed. The collected data will be saved as a TIFF file. TIFF files are very large, but contain all the raw data in the collection without compression.
Object method programming languages are widely used in artificial intelligence applications used in drones. Thanks to the libraries in C++, matlab and Python programming languages, the number of resources you can access is quite high. For this reason, we will also use these programming languages.
Space Agency Data
Soil moisture data produced by the European Space Agency's Climate Change Initiative will be used in the project.
Hackathon Journey
In this application, we had fun, we faced difficulties. It was an event full of new experiences for us. We learned a lot about NASA's work and listened to informative seminars. Our purpose in choosing this project we want to put a stop to the climate change that brought the world to an end. We approached this project by doing detailed research and talking about what to do. When we encountered a problem, we tried to solve it by doing more research on that problem. A big thank you all our teachers and coordinators who have worked hard to help us.
References
1- ‘Design and assesment of new artificial references surfaces for real time monitoring of crop water stress index in maize’ , Agricultural Water Management, 2020, https://doi.org/10.1016/j.agwat.2020.106304
2- ‘Drones for Conservation in Protected Areas: Present and Future,’ Drones, 2019, https://doi.org/10.3390/drones3010010
4- ‘Precision Agriculture Techniques and Practices: From Considerations to Applications’ Sensors, (Basel), 2019, https://doi.org/10.3390/s19173796
Tags
#drone technology, #climate change, #artificial intelligence