ProFarm
ML and Django Based Web Application to predict suitable Crop for particular conditions and Location based Yield Calculator / Predictor
Created on 20th March 2022
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ProFarm
ML and Django Based Web Application to predict suitable Crop for particular conditions and Location based Yield Calculator / Predictor
The problem ProFarm solves
Lack of knowledge and proper resources has always been a problem over all these years to our farmers. Not knowing what is best for the field, being totally unaware of what could happen in the future leads to thousands of acres of land going waste every year.
To tackle this problem of the farmer, my project specifically targets the crop prediction segment so as to make the farmer aware of what's best for the land he owns, and also give him approximate yield in advance itself based on parameters input.
As we are still in a development stage of Precision Agriculture, I am trying to implement it from the base itself. As sowing is one of the first steps after soil-preparation, it is necessary to create a model for crop-related guidance, henceforth I present to you - Profarm
My Project allows the farmer to save time, and have a confident decision about what crop he should grow and not get confused due to random advices. It also increases efficiency as the farmer can spend the saved time to properly manage the grown crop so that he gets a good yield.
Challenges I ran into
The First and Foremost challenge was learning ML. Having no background in this stack, I started from zero 7 days back from now, learnt a few models, then learnt about how to visualize data using matplotlib. It wasn't easy due to lack of proficiency in python, but gathering information from various sources, and support of mentors and teachers allowed me to reach this stage where I was able to create a fully functional project.
Another massive challenge was Deploying the Django Project. I spent almost 7hrs trying to deploy it, tried almost every approach - Apache, Nginx, GCP Django Stack, IIS, etc. but nothing seemed to work. Then I took some rest to relax my mind, and after getting up, watched the process again and finally deployed it using Nginx and GCP. Definitely it was tiring, but at the end its all worth it when you see that website live.