Inspiration

Whenever talked about conserving wildlife we mostly think about saving our endangered animals and mostly neglect our endangered plants. This is our main motivation for choosing Problem.

What it does

  1. Ability to link three databases for the efficient search algorithm- i)Database containing CITES-listed plant species ii)Medicinal plant service to retrieve trade or pharmaceutical names iii) eCommerce platform to monitor offer for a product.
  2. Results in tabular form
  3. Easy and semi-autonomous

How we built it

As a solution to the problem, we started with analyzing our CITED listed endangered plants and to our surprise, there are more than 30,000 plants. We further mapped this data with our MedicinalPlantNameService which is a search engine for our medicinal plants from this we get the common names and scientific names of all the medicinal endangered plants. Now our main challenge is to make an autonomous tool that does efficient searching of such plants on our eCommerce platform who all are selling this type of product which can further be used to understand the scale of trade. We've used the dynamic searching algorithm KMP for fast searching and kept our UI simple for a non-technical user. Since we are taking the data from the web scrapping it is easy to accommodate any changes further.

Challenges we ran into

  1. Getting the data from the eCommerce platform because of security checks.
  2. Mapping of data from one database to other
  3. Developing an efficient search algorithm that is capable to automatize the search for products.

Accomplishments that we're proud of

  1. Successfully fetching the data from the search engine and eCommerce platform by web scrapping
  2. The dynamic approach that is being followed for searching to save time.

What we learned

  1. Web scrapping from the search engine
  2. Building a self-autonomous tool
  3. Working on multiple datasets
  4. Mapping of one dataset to other datasets

What's next for IN-CITES

  1. Implementing Machine learning algorithm
  2. Text data analysis

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