Inspiration

This project was inspired by the growing need for smarter and more accessible farming tools. Many farmers, gardeners, and regular households do not always have an easy way to understand what their plants need. We wanted to create a simple system that could help people maximize plant growth, reduce wasted water, and improve crop or garden output using real-time environmental data.

What it does

SoilTune is a smart soil-monitoring system that helps users understand the condition of their plants and soil. It measures important factors such as soil moisture, water level, temperature, humidity, and pressure. By collecting this data, SoilTune gives farmers and home growers a clearer idea of when their plants need water and whether the growing environment is healthy.

How we built it

We built SoilTune using an ESP32 microcontroller connected to multiple sensors, including a soil moisture sensor, water level sensor, and environmental sensor for temperature, humidity, and pressure. The sensor data is read by the ESP32 and displayed through a simple web interface, allowing users to check their plant conditions wirelessly. The physical design also includes a stake-style enclosure that can stay in the soil, with a small solar panel concept for outdoor use.

Challenges we ran into

Several challenges we ran into involved getting our database working properly for machine learning and making sure our AI model could reason with the sensor inputs in a meaningful way. This became one of our biggest bottlenecks because the system depended on collecting reliable data and using it to make accurate decisions. We also faced Wi-Fi connectivity issues with the ESP32, which made it difficult to consistently transmit sensor readings.

Another major challenge was avoiding false alarms. Since our system is meant to help users make decisions about plant health, we had to think carefully about how to collect and interpret data without giving misleading results. Finally, we went back and forth on the product design itself. We wanted SoilTune to feel user-friendly and realistic for regular households, while still having the potential to support farmers, without making the system overly expensive or complicated.

Accomplishments that we're proud of

One accomplishment we are proud of is creating a concept that addresses a basic but extremely important need: food. Food is essential to people’s lives, the economy, and global stability. As climate change leads to hotter days and more unpredictable growing conditions, it is becoming riskier and more difficult for farmers to plant, maintain, and harvest crops effectively.

We are also proud that our sensors were able to collect useful data that our model could reason with successfully. This showed us that SoilTune has real potential as a decision-support tool for plant care. In addition, we designed a website that was not only user-friendly, but also interactive, making it easier for users to understand their plant and soil conditions.

What we learned

We learned that designing technology for agriculture is much more difficult than it may seem. Agriculture involves real-world conditions that can change quickly, so building a system that is accurate, affordable, and useful requires careful design. Through this project, we gained a better understanding of how sensors, data, machine learning, and product design can work together to help both homeowners and farmers. Most importantly, we learned that systems like SoilTune could give users better insight into how to care for their plants and potentially maximize crop yield by making plant care more data-driven.

Most importantly, we learned that systems like SoilTune could give users better insight into how to care for their plants and potentially maximize crop yield by making plant care more data-driven.

What's next for SoilTune

We want to start by focusing on household users and small gardens, then eventually expand toward larger agricultural applications. In the future, we hope to improve SoilTune with a better enclosure design, higher-quality sensors, solar power integration, and more advanced software features that can provide stronger recommendations for plant care.

Built With

  • backboard
  • gradio
  • io
  • k2-think-v2
  • langchain
  • lovable
  • supabase
  • tanstack
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