Sustaining Our Planet for Future Generations
reSTORE - reTHINKING Life
Summary
By the year 2050, nearly 80% of the earth's population will reside in urban centres. Applying the most conservative estimates to current demographics trends, the human population will increase by about 3 billion people during the interim. An estimated 109 hectares of new land will be needed to grow enough food to feed them. (SOURCE: NASA)This limits the place available for flora and fauna around us, and the vegetation plays an important positive role in atmospheric purification and air pollutants reduction.We as a team have devised a solution for the mentioned problem sustains the planet for our future generation, which solves a major challenge ahead of us.
How We Addressed This Challenge
Our solution uses an air quality sensor and recommends air emission plants(air pollutant absorbing plants) and pest repellent plants which absorb the air pollutants as shown by the air quality sensor and we recommend plants best suited for the customer's enviornment/workspace using unsupervised ML model(clustering), and take the feedback from the users(labels for the dataset) and then when we have enough plants+labels, we shift to supervised ML models, because it trains on the data provided by the previous users, thus sustaining our planet for the future.
These plants can then be vertically planted mainly in corporate offices and schools. In addition to that, they can also be planted in apartments and houses too. We can incorporate this as a Service-Based Business(SBB). We can contact unemployed gardeners whose source of income has been affected due to COVID for maintaining and shaping these plants, and contact farmers for wholesale plants, thereby increasing their income and increasing our profit margin. We can follow a drip irrigation system in which the drained out wasted water from the plants can be used as drinking water by filtering it out. If there is a lack of sunlight in the room, then we can use fluorescent light and LED's.
We hope to achieve a greener and more fresher enviornment for our future generation by sustaining our planet from the harms of climate change.
How We Developed This Project
As time passes by swiftly, we realsie that the future generation and the current generation have seen a decline in the greenary around us. We are always around 4 walls. So, we thought due to the lack of space and the hazardous air quality index in India, we could devise a solution that solve this and we came up with the idea of recommending companies, instituitions and other people plants based on the air quality index around their living space for vertical gardening.
APPROACH:
- Designed an Empathy Map.
- Made sure we knew the tools required to create this
- Started looking for the required dataset and started researching about our idea
- Finalised our dataset, components for the IoT device, UI/UX
TECHNICAL ASPECT:
Tools:
- Animaker - to create the video
- https://creately.com/usage/empathy-map-templates/ - to create the Empathy Map
- Canva - to make the ppt
- Circuit.io - to design the circuit
Coding Languages:
- C++
- Python
- NodeJS
Hardware:
- Arduino Mega
- MQ2, MQ8, MQ135
- Adafruit MiCS5524 and SPG30
- 16x2 OLED Display
Software:
- Jupyter Notebook
- VS Code
- Arduino IDE
- Fusion 360
- Solid Edge
PROBLEMS:
One of the major problem which we faced was finding dataset, which we did later on but had limited rows and columns which wasn't enough to train a ML model so we manually added some data and used it to cluster the data, later on we are thinking of training the data once we get data from the users.
How We Used Space Agency Data in This Project
We used the data available on NASA's data portal on Air Emission plants in order to recommend the users the best plant for their surrounding to use it for Vertical Gardening.
Project Demo
PPT - https://drive.google.com/file/d/1oVCyRCrNWj7kvsg1H1olTPdMojhqvSX8/view?usp=sharing
Video - https://www.youtube.com/watch?v=dgqYahDZZFU
Data & Resources
https://github.com/Mohammadsalahuddin/indoor_air_quality_monitoring/tree/master/lib
Since the NASA Data Portal wasn't opening in our laptops we used, https://catalog.data.gov/dataset/air-emission-plants