SEAR Program
What is SEAR?
The Science Experiential Aerial Research or SEAR for short is a high school program using drones equipped with sensors to collect aerial data in certain areas and machine learning to identify and eliminate Dutch Elm Disease, a fungal virus that spreads via elm bark beatles and any other viruses that wish to cause harm.
SEAR DAY 1
We first met the person running the SEAR program named Matt Johnsen, an ex military captain from the Canadian Air Forces who had an interest into drone work. He started to then talk about his backstory about how he started a drone company used to take pictures in 2015, doing mulitple jobs for real estate companies but then started helping farmers with their crops to find any diseases found within the crops to prevent any type of virus to go haywire and cause havoc everywhere.
We would then learn about how drones work like key components such as the IMU (Internal Measure Unit) which is how the drone is able to sense things and auto correct it's positioning if need be. Another thing we also did learn about is the actual viablity of a drone and how a simple flying machine can take you places.
SEAR DAY 2
This lesson actually started as an online lecture which I wasn't expecting but how Geographic Information Sytems (GIS) work. We learned some more basics about drones like longitude and langitude which are the up, down, left, right in simpler terms.
Another thing that we did learn about was the types of electro magnetic waves and how big, far, and strong their connections were. I had some fun learning about this topic because I was not expecting certain waves to have such a big gap between each other.
SEAR Day 3
For our third day, we had another online lesson about how a machine can learn and adapt its ways. An ai can only evolve/learn new things is if a human can do it themselves, an example being able to identify certain types of plants without getting confused with something else.
One thing we did learn were four main things a machine will consider whenever processing their information, is a false negative, true negative, false positive, and true positve. This all sounds weird so let met explain: These terms are essentially an AI's building blocks to connect what's what such as being able to decipher the difference between a plant and what is not a plant. At the start they can seem pretty unitelligent but with every error it makes, the more precise and smarter it gets which was really interesting to know about machine learning is essentially continuious attempts of trial and error until you get it right.