Inspiration

I looked into the food security crisis in Africa. I researched about their difficulty in cultivating crops due to lack of modern farming techniques, information and often even the internet. I read about the success of Agrix Tech, PlantVillage and others to understand how ripe the field of digital agriculture can be.

What it does

It is quite in simple in its concept. It makes use of crop features to predict the Disease Prevelance in the plant. When it is production ready, I would like to be able to use annual data to predict which crops are most likely to catch diseases. After getting this knowledge, the farmers can avoid these crops and focus on those that have the best chance of being healthy based on their location, available crops, planting date

How I built it

I relied on Google Colab and Python to build this project.

Challenges I ran into

The biggest challenge was finding the right datasets. I spent hours searching websites and found some very useful information, but often they were not in the right format for me to use.

Accomplishments that I'm proud of

I am proud to be almost finished with core tech. With a bit more data wrangling and machine learning implementation, this can be a very robust application.

What I learned

I learned how to build machine learning models and deploy them. I became more familiar with different libraries and tools available for AI work. Most of all, I think I learned a lot about how to seek advice when things are not making sense. It is not about getting the answers outright. It is about understanding where you went wrong in your process and also what other steps could be taken to become more efficient

What's next for PlantVillage

I would really like to deploy the app on a phone. I want the application to store the annual data before it gets into the hands of farmers so as to reduce network dependencies. I understand that small scale farmers may not have any internet connection so this app needs to be effective without being online.

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