Inspiration
While using python we came up with various machine learning, image recognition techniques and cognitive visions to help other communities
What it does
It enables farmers to monitor their crops, keep them healthy by detecting any disease in them and find the best fit crop for their soil.
How we built it
We built it using Python and Flask (Python framework)
Challenges we ran into
Finding the best-fit machine learning algorithm fro image recognition and disease detection was the toughest task. Further more, we had to use hyper-parameter tuning to increase the accuracy and to fine-tune our chosen algorithms for predicting the best fit crop for the farmer's soil was very cumbersome as initially we had a very low accuracy
Accomplishments that we're proud of
We performed image recognition for the first time using various cognitive techniques to which we were very new.
What we learned
We are now familiar with various machine learning algorithms which we tried to implement
What's next for CropDoc
We are currently using a static database which provides us only old images of the plants. But we are further planning to make it real-time by extending its IOT side.
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