fertiCulture

correlation-matrix
pair-plot

Inspiration

Our inspiration for FertiCulture stems from the pressing need to address challenges faced by farmers in managing soil health and subsequently optimizing fertilizer use. We have recognized the information gap and lack of personalized guidance for farmers. Michigan, being a primarily agricultural state faces this problem to a higher degrees, and we aim to empower them with a solution that leverages technology to revolutionize agricultural practices. Another issue that this project contributes to is climate change - by reducing the detrimental impact of fertilizer overuse on the climate, FertiCulture is making the world cleaner, one crop at a time.

Michigan Farmers struggling with fertilizer usage

What it does

Predicting Optimal Nitrogen Value

Taking dependent variables like temperature, humidity, pH, and rainfall, and using data classification methods and Machine Learning to predict the optimal Nitrogen value (in the fertilizer) that needs to be added to the soil. Farmers can then use this information to reap the full benefit of fertilizers while keeping the drawbacks to a minimum.

Finding Crop Yield

Taking dependent variables like crop weight, crop moisture, crop type, and harvested area, we can identify the yield of the particular crop. This would work especially well in the pre-sowing stage where farmers can identify which crops can yield the most in a limited harvesting area.

Resources/Information

Another feature of our application is the resources page, which provides links for farmers to access. The links help the farmers access tests and information regarding soil health, fertilizer health and the US Department of Agriculture.

How we built it

*Backend: *

Used python via Google Colab and Pycharm
Utilized multiple datasets containing NPK values of crops, along with temperature, humidity, pH, and rainfall values.
Created our dataset containing crop moisture and weight of the crop.
Imported modules like Pandas, NumPy, Matplotlib, and Seaborn
Created multiple scatterplots and correlation matrices to visualize the Actual vs. Predicted Regression.

*Frontend: *

Used python via Pycharm and Thonny
Imported kivy module
Created separate .kv files called main.kv and splash.kv
The main.kv file is where we formatted the main screen with a bottom navigation bar, icons, colors, accents, etc.
The splash.kv file is where we formatted the loading screen (splash screen).

Challenges we ran into

Data Variability: Dealing with the diverse nature of soil data and farming practices posed challenges in creating a robust algorithm.
Integrating our front-end and back-end
Importing relevant modules
Attaining a high accuracy on our concentration predictor -Miscellaneous Python bugs

Accomplishments that we're proud of

-Climate Change Mitigation: We aimed to amplify efforts to curb global warming. According to an article by MSU Today, fertilizers contribute greenhouse gases to the atmosphere, and excess usage of these fertilizers makes the effects even worse. By providing a data-driven approach to predicting the ideal amount of fertilizer, or the ideal crop that can be grown with negligible fertilizer, we offer the agriculture sector to contribute towards sustainability. Moreover, excess usage of NPK damages the topsoil, a practice that is not sustainable. -DEI Applications: Farmers in the United States are overwhelmingly white males. Some sources say close to 95% of agro-based workers fit in the above demographic. Ease of access is important in this field, and this application offers other demographics, particularly women and racial minorities, to get involved in the agriculture sector. Farming is usually considered taboo in many places, although, it is a pretty lucrative business. This app can guide and offer them resources (possibly more after expansion). -Community Applications: Food security affects our communities worldwide. Farmers in Michigan are affected by excess fertilizer usage. We are giving back to the community of farmers here in Michigan, while also keeping up with MSU's history of starting with an agriculture-based curriculum. From Aggies, to Spartans. -Learning New Technologies: Summarized below :) We made a pretty sweet hack in 24 hours with minimal experience. We're proud of that!

What we learned

As freshmen, we were beginners to the world of Python and hackathons. In 24 hours, we successfully learned (yes, learned, turns out that's not new) and implemented the:

-kivy module -data classification -Machine Learning -Github repositories -modules like pandas, matplotlib, mediapipe, numpy

What's next for FertiCulture:

-Evaluating more datasets to predict the value of phosphorous and potassium along with nitrogen, which we couldn't complete within 24 hours. -Hardware accessories like sensors and Arduinos to complement our project and make it accessible for farmers. It can detect soil content in real-time, and that will be helpful.

References

Built With

kivy
machine-learning
pandas
python
random-forest-classifier
regression
scikit-learn

Submitted to

Created by

I worked on the front-end and ideation portion of the project. I used the KivyMD module to create the interface, and integrated the machine learning portion (the back-end) with it.

Lowell Monis
I worked on the front-end as well as the design aspect of the project. Learning and
implementing the Kivy Module, creating logos, making presentations, as well as preparing the pitch were some of responsibilities.

Tarun Rawat
zainsuch19 Suchedina
Adhiraj Chanana