Irrigation recommendation system

Inspiration

The idea of working on this project came after realizing the climate change issues that reduce agricultural production. Most of the time, when there is low rainfall, farmers irrigate their crops to increase their productivity, and they make a plan schedule or use their experience to know whether they have to irrigate or not. and this action leads to the misuse of water resources and increases the cost. after realizing that, I came up with the idea of how I can create a system that can help farmers by recommending whether they have to irrigate or not based on different environmental conditions.

What it does

The web application I created will assist farmers by recommending whether they should irrigate their crops or not. all results should be displayed on the dashboard main page. this will not only manage water resources but also will reduce cost, boost crop health, and enhance production.

How we built it

The system was built using Python programming language, and I created a logistic regression model, which is suitable for classification problems, but this was done after cleaning the data. later, I passed both the preprocessing stage and the logistic regression model into a pipeline to enhance its usability. after that, I saved the model. Then I created a web application using Streamlit, and later I integrated the machine learning model into the web application. all these; helped me to get the system, and after I deployed it, so that it can be used by all deliverables.

Challenges we ran into

The challenges I faced was to get appropriate data to use while creating the system and the computer sometimes froze while working on the project.

Accomplishments that we're proud of

After creating this system, I was very happy to take part in contributing to the innovation and building something that can help my community as a whole. Not only that, but also showcasing the skills by applying them enhances hands-on practices, which is a big milestone to celebrate.

What we learned

Through this project, I have learned the essential metrics required by crops to be healthy and the way to build all tech solutions that can help the community. These was fruitful days to remark.