Inspiration
Our inspiration behind Sook Sook came from the observation that many people struggle with maintaining healthy plants. Whether it’s due to inconsistent watering, inadequate lighting, or simply not knowing the right conditions for different types of plants, plant care can be challenging.
We wanted to create a solution that makes plant care accessible to everyone, no matter their experience level. We were motivated by the idea of helping plant lovers—especially busy ones—automate their plant care routines so they can enjoy the benefits of plants without the stress.
What it does
Sook Sook is a smart plant incubator powered by AI and computer vision. Here’s what it does:
Monitors environmental conditions such as temperature, light, and soil moisture using sensors.
Measures plant height via computer vision with a webcam, providing feedback on plant growth.
Automates watering based on real-time soil moisture data and temperature, adjusting watering duration for optimal plant health.
Controls light with a servo-driven lid that adjusts based on light intensity detected by the system, ensuring the plant gets enough light at the right times.
It’s designed to make plant care easier, especially for those who may not have time to constantly check on their plants
How we built it
Building Sook Sook involved integrating hardware and software components:
Hardware:
We used an Arduino microcontroller to connect the soil moisture, temperature, and light sensors. These sensors collect real-time data to inform decisions such as watering and light adjustments.
A webcam is mounted to capture images of the plant for computer vision tasks, which measure plant growth.
A servo motor controls the lid, adjusting it based on light levels, ensuring the plant receives optimal lighting.
Software:
Python was used for the main control logic, using libraries like OpenCV for computer vision to measure plant height.
AI algorithms are employed to adjust watering durations based on temperature and moisture levels.
We also built a website where users can upload plant images, track growth, and share their plant care experiences with others.
Challenges we ran into
Calibration of Sensors: One of the first challenges was ensuring accurate sensor calibration. The soil moisture sensor, for example, needed to be carefully calibrated to work reliably across different soil types and plant sizes.
Computer Vision Setup: Setting up the plant height measurement using computer vision was tricky. We had to ensure that the camera was positioned correctly, lighting was consistent, and the plant was always in focus. There were also challenges with plant recognition due to the plant’s growth stage and light interference.
Light Control Algorithm: The servo-driven lid needed to adjust precisely based on light intensity. This required fine-tuning of the light threshold values and making sure the lid opened and closed smoothly without affecting plant health.
Accomplishments that we're proud of
Real-Time Monitoring & Control: We built a system that automatically adjusts the watering cycle and light exposure based on real-time data from the sensors. This is a major step towards creating an autonomous plant care solution.
Plant Growth Tracking via Computer Vision: The computer vision system that tracks plant height works accurately and can provide users with growth insights, making the plant care process more transparent.
Website Development: We successfully created a platform where users can post their plant images, get advice, and track their plant’s growth. This social feature fosters a community of plant lovers and provides additional value beyond just the technical automation.
Educational and Accessible Plant Care: Our system makes plant care accessible to a wider audience, especially those who might be new to gardening or who have limited time to care for their plants.
What we learned
Integration of Hardware & Software: We learned how to integrate multiple hardware components, such as sensors, motors, and cameras, into a seamless system. Managing the interactions between hardware and software in real-time was a significant learning experience.
AI & Computer Vision: Implementing AI and computer vision for plant height measurement introduced us to challenges in image processing, calibration, and real-time analysis.
User-Centered Design: We also realized the importance of user experience in a projecMulti-Plant Support: We plan to expand the system to support multiple plants, each with its own care needs. This will involve adding more sensors and adjusting the algorithms to accommodate a variety of plant types.
What's next for sook sook
Mobile App Integration: We’re working on integrating the system with a mobile app, allowing users to monitor and control their plants remotely.
AI Growth Prediction: We’re looking to incorporate machine learning to predict plant growth, detect potential problems earlier, and even provide personalized care recommendations based on the plant’s history.
Plant Disease Detection: We’d also like to integrate AI-based disease detection, using computer vision to analyze plant images for signs of diseases or pests, offering proactive solutions to plant owners.t like this. Ensuring that the system is intuitive, the website is easy to navigate, and the sensors work reliably in various environments was essential for the project’s success.
Collaboration & Problem-Solving: Working as a team, we learned how to approach complex problems collaboratively, troubleshoot issues, and iterate quickly to find solutions.
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