This week, we all worked on our individual components to present on the Interim Demo. I finished my fruit/ flower detection and improved it so that it can find the most common types of flowers and fruits by working with the most common flower and fruit colors. I combined the fruit/ flower detection that I implemented with HSV Color Detection, and the pixel per metric function to make a growth classification system that will use these two parameters to notify the user on whether a new growth stage has been reached. The notifications are sent to the website and the user’s phone number through the Twilio API. I also made a program where the plant is separated from the background using Edge Detection, then the fruits or flowers are wiped out with HSV color detection to get the leaf alone. I am currently testing for the program to be able to detect white and discolored brown spotting, which is a common plant disease pattern, as well as withering. Further, I realized that I could be more specific with my testing metrics such as the accuracy of my pixel per metric when predicting the real height of the plant, so I will change some of my metrics to be more specific and critical of my application.
I am a bit behind schedule, as I would’ve liked to have my application run smoothly by Friday, but I still have to debug the disease detection and the Twilio API notification sending system. I will spend my weekend doing so and will have these functionalities prepared for the Interim Demo.
Next week, I hope to start on the plant vine and stem bending algorithm and test that out the following week. Based on the critiques from the Interim Demo and my own judgement of the quality of my CV application, I will change parameters and fine tune my application in the remaining weeks. I also plan on making stricter testing requirements and testing the CV application with these metrics in mind.
