December Results

Through our sensor testing and experimentation, we were able to draw several conclusions and develop goals for next semester. This semester provided a good launching point for us to dive into more details. With more time and investigation, we will be able to develop a sensor that can be used to reduce water usage at racetracks all over the world. Below are summaries of key lessons learned from testing, next steps for our project, and our December Project Poster.

Sensor Depth and Orientation:

During our testing, we determined the sensor needs to stay completely covered by the soil for best collection results. In our application, the sensor we used measures moisture by reading capacitance between two nodes. If the sensor is placed at too shallow of a depth or if by movement it creates a ditch behind itself, then the readings will be skewed and the sensor will not be able to accurately measure soil moisture. Therefore, we plan to design a mechanism that will flow soil onto the back of the sensor and prevent the ditch effect. As our understanding of the sensor reliability grows, we would like to explore the affect of the angle on the data output in further detail. With a greater understanding of the best angle, we may be able to find a solution to the 'ditch effect' without having to design a mechanical device. In regards to the tests conducted between sideways and forward sensor position we could not gather consistent data in the sideways position. However, it may still be a plausible position through further development.

Arduino Program:

Development of the Arduino program was relatively straight forward. Our sensor has a defined output of 0 - 1023, where the higher the moisture content, the lower the number. Finding a calibration curve for our sensor will be vital for the success of our prototype. When the sensor was immersed in water it gave a reading of around 100 rather than close to 0 as we predicted. The determination of moisture content present in soil will need to be calibrated through several rounds of testing. This is important to calibrate so we can reach our goal of understanding the optimum range of 10-20% track moisture content.

Track Prototype:

The current prototype track is too small to gather all the data we need. The current track is about 1 meter long and when moving at about 5mph, the estimated speed of a tractor on a track, the end of the track is reached rather quickly. With a larger track we will be able to see how data is affected through more exposure to soil. We will be able to determine more failure modes and develop countermeasures for actual racetrack conditions. In order to analyze and obtain all of our objectives, we will have to find a more effective track prototype.

• Sensor should be covered sufficiently with soil to feedback accurate data
• Different mounting angles yield different results
• Sensors must be calibrated to work for optimum track moisture of 10-20%
• Testing is needed on a larger track to understand data over longer stretches
• Flowing the soil over the sensor could increase accuracy as more soil is touching the sensor at a time
• Overall, non-contact sensors are far more expensive and harder to implement than contact sensors

• Order parts and assemble prototype
• Write code for data acquisition and storage
• Test prototype on racetracks against benchmark sensor
• Test angle of sensor
• Test data collection rates
• Test GPS accuracy
• Develop web app to collect data and control prototype
• Iterate prototype based on tests (possibly integrating with method to flow soil across sensor)