Inspiration
In light of the pressing global environmental challenges, our journey led us to investigate various underlying issues that demand our attention and resolution. Among these, one critical concern that has often been overlooked and remains a significant source of environmental hazards in India is the mismanagement of tree diversion and enumeration. The continued reliance on outdated and traditional methods in this context has further exacerbated the issue.
What it does
Issue: When industries or factories are established, government approval is required to remove trees from the designated area. The government mandates that for each tree removed, an equivalent number of trees must be planted as compensation, as per their policies.
Traditional Method: The current tree enumeration process relies on manual labor to count trees and identify their species by marking tree trunks. This method has several drawbacks, including the potential for corruption during tree relocation and the absence of guarantees regarding the replacement of trees with the same species.
Solution - TreeDex: TreeDex is an innovative project that introduces a technological solution to this problem.
Satellite Imagery: TreeDex utilizes satellite imagery to capture aerial views of the area under consideration.
AI Model: TreeDex incorporates a highly trained AI model. This model not only counts trees but also provides detailed characterizations of each tree.
Accurate and Transparent: Using this advanced system, TreeDex offers precise and transparent tree enumeration, ensuring that every tree is accurately identified and quantified.
How we built it
we created an AI model using deep learning, CNN algorithms. we created our own dataset and deployed it with flask and our UI with react for the first time.
Challenges we ran into
1)issue in the accuracy of the data 2)issue in collecting a large dataset 3)Showing data analytics on UI
Accomplishments that we're proud of
1)Trained our model on the counting technique 2)Collected a large no. of dataset 3)integrated with flask
What we learned
1)learned about Unet and image monitoring 3)learned about integration with flask
What's next for TreeDex
1) we would try for government TieUp 2)Air index model which would tell about the air quality according to no. of trees.
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