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Jana’s Status Report for 03/08/2025

This week, I worked on my part of the design report which included the abstract, introduction, use-case requirements, design requirements, ML-specific design trade studies, ML-specific testing and validation, schedule and task division, and the related works section. 

On the technical side, I completed labeling the datasets consistently, ensuring uniformity across images from multiple sources. I also finalized the data augmentation techniques, including rotation, greyscale conversion, and flipping, and have begun implementing them. As a result, I now have a fully labeled dataset ready for model training.

Additionally, I established a plan for testing live plants. I am monitoring eight plants under different conditions to support model training and evaluation. This includes 2 plants being underwatered, 2 being overwatered, 2 with nutrient deficiencies, and 2 healthy plants.

I am slightly behind schedule as I have not yet established the initial training framework for the model due to midterm exams and other deadlines. To catch up, I will continue to work through spring break and dedicate additional hours over the next week to make progress.

Next Week’s Deliverables:

  • Establish the initial training framework for the model
  • Test different model architectures (ResNet18 vs MobileNet) using the available dataset
  • Compare model performance on greyscale vs RGB images
  • Continue monitoring live plants for testing and validation
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Yuna’s Status Report for 03/08/2025

This week, I spent half of the time working on the design report and the other half on the web application. I mostly worked on design trade studies, test and validation parts, and parts that need explanations about web app. I revised it with the team at the end.

For the web application, now I have a rough outline for everything. I spent most of the time reading different react documents and learning how to use different libraries. I have a working frontend that displays plant info and backend that has plant models, though I will still need to work on specific frontend designs using bootstrap. (Right now the website has very basic components)

I am on schedule. As planned in the schedule, next week I will start working on the data transmission part on the web application. The rough web application is set up, but the sensors and APIs are yet to be integrated. I will improve the design and UIs as I integrate those into the web app.

Next week’s deliverables:

  • Implement django channels (websocket).
  • Replace the current database with AWS dynamoDB.
  • Integrate RaspberryPi with web app.
  • Keep improving the design of the app.
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Yuna’s Status Report for 02/22/2025

This week, I specifically worked on Solution Approaches and Testing, Verification, and Validation part in the design presentation. My team and I ordered different plants and additional components for the plant care, including soil and  liquid nutrients. Our team also assembled the greenhouse together.

I designed a rough user interface, and currently have a partially working web app. Here’s the rough UI design (it’s high likely to change in the future):

I did more research about what specific AWS services to be used for data transmission between the web app and the microcontroller. For database, we were planning to use AWS dynamoDB, but after research I realized we could also use AWS IoT to connect dynamoDB with RaspberryPi.

I am currently on schedule.

Next Week’s Plan:

  • Finish frontend and backend – have a working web app by the end of spring break
  • Explore more about how data will be transmitted from/to the web app (AWS services, HTTP/MQTT)
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Zara’s Status Report for 02/22/2025

This week, I focused on setting up the Raspberry Pi at home and configuring it for our project. I successfully set up a Git repository for the team so we can track our code and collaborate efficiently. I also wrote initial code to collect temperature and humidity sensor data. The temperature readings appeared accurate, but the humidity values seemed off. To investigate further, I plan to rerun tests and, if the issue persists, compare results with backup sensors to determine whether the problem is with the sensor itself or the way the data is being processed. In addition to working on the sensor data, I also started implementing the Plant API data retrieval code, though I haven’t finished it yet. Here is an image of the sensor output data:

As a team, we received most of our components and assembled the greenhouse, ensuring that all parts fit together correctly. We also finished ordering the remaining components, including the plants, so we can begin testing as soon as they arrive with the sensors and start collecting training data for the ML models. My progress is currently on schedule since I have been able to start testing the sensors, though I still need to complete the Plant API integration this week.

Next week, I plan to set up and test the remaining sensors to ensure they are providing accurate readings. I also need to configure the Raspberry Pi to connect to the school’s Wi-Fi so that I can work on it remotely while on campus. Additionally, I aim to complete the integration of the Plant API so we can start using the data in our system.

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Team Status Report for 02/22/2025

There were no major changes to the existing design.

Challenges & Mitigation:

  • Datasets for ML not having all necessary data: There were no single datasets that have all the data we needed for health detection, so multiple datasets were selected to be combined. 
  • Potential plant loss during training: We planned to let the system support three plants last time. In order to ensure a larger dataset and be ready for plant loss, we ordered 4 plants for each plant type. We are planning to set up the sensors and actuators as quickly as possible.
  • Increased costs: We ordered plants and additional components (blackout window film, liquid nutrients, soil, basil plants, hamalayamix foliage plants, flowering plants), which was a larger increase in costs than we expected. To mitigate this, we found the cheap but appropriate components to be purchased and minimized the amount of extra components like soil.

Progress:

  • Finalized and ordered plants and additional components for growing plants.
  • Assembled the greenhouse.
  • Worked on code that runs sensors.
  • Started working on the design review report. Split up sections of the report for each person.
  • Keep working on ML integration and web app.

Next Steps:

  • Keep setting up temperature/humidity sensors and start setting up a heater.
  • Finalize web app and user interface.
  • Establish initial training framework for the ML model.
  • Keep working on the design report.
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Jana’s Status Report for 02/22/2025

This week, I worked on my part of the design presentation which included defining the quantitative design requirements. I also chose and ordered the live plants for the project, which means we now have a clear direction for data collection and analysis.

For the plant health prediction ML model, I selected the datasets to be used in training, opting for images of entire plants instead of just leaves as originally planned. As many of the available datasets didn’t have all the necessary data, I chose to combine multiple datasets to include the required abiotic and biotic stress factors. Since the dataset consists of full plant images, the leaf detection step may not be necessary for now. Instead, I plan to train the ML model on these images directly and will only preprocess the images if needed, based on model performance.

I explored several potential models for plant detection and health classification. YOLOv8n/YOLOv8s stand out as suitable options for object detection. These models would allow the identification and labeling of multiple plants and their individual parts within the images, which is suitable for our 3 plant system. Additionally, I’m considering ResNet50 for better overall performance or MobileNet, which is more lightweight and better suited for deployment on an RPi 5. My plan is to train multiple models and evaluate which one performs the best.

I am on schedule.

Next Week’s Deliverables:

  • I will start labeling the datasets consistently, given that the images come from multiple sources. This will ensure that the training data is standardized. 
  • I will also perform data augmentation to create a larger training set.
  • I will also establish the initial training framework for the model.
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Zara’s Status Report for 02/15/2025

This week, I focused on ordering all the necessary parts for our project and ensuring that the components we selected are fully compatible with our Raspberry Pi 5. I spent time carefully planning how the sensors and actuators will connect to the Raspberry Pi on the pinout, including how they will be powered and controlled. Additionally, I researched various water pump systems to determine the best way to integrate one into our design.

With the upcoming design presentation, I also worked closely with my team to refine our slides. I finalized the block diagram to visually present how the system components will connect and interact. To address previous confusion about the role of our machine learning model, I created a detailed flow diagram to clearly outline its function and integration within our system. My goal was to ensure that our audience would fully understand how our data inputs contribute to plant health classification.

Currently, we are on schedule, and our progress aligns with our planned timeline. However, I am slightly concerned that some of our ordered components may take longer than expected to arrive, which could delay the integration of certain parts. Fortunately, none of these delays seem significant enough to disrupt our overall schedule. Another potential issue is the soil nutrient and pH sensor we selected, as it has limited reviews and its reliability is uncertain. Due to budget constraints, we opted for a more affordable sensor, but if testing reveals any issues, we will order an alternative model as soon as possible.

By next week, I hope to have the temperature and humidity sensor set up and fully integrated with the RPi 5 to establish a working model for collecting environmental data. Additionally, I plan to begin testing other hardware components as they arrive to ensure a smooth transition into the next phase of our project.

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Team Status Report for 02/15/2025

The most significant risk this week was potential plant loss during model training or system adjustments, which could disrupt data collection. We are managing this by scaling up the system to support three plants instead of one, ensuring a larger dataset and providing backup in case any plants die. Additionally, we are mitigating this risk by setting up the sensors as quickly as possible to begin data collection early. This was the most significant change to the design.

Challenges & Mitigation:

  • Conflicting sensor data: To ensure uniform readings, we placed all plants in a single large pot, allowing us to use one set of sensors and actuators for all plants.
  • Limited Raspberry Pi pins: We selected multi-function sensors (e.g., a 3-in-1 soil pH, nutrient, moisture sensor, and a 2-in-1 temperature/humidity sensor) to reduce the number of connections needed.
  • Increased costs: A larger greenhouse ($103.99) and increased water capacity were necessary to support three plants. To offset this, we are minimizing additional costs by reducing the number of sensors and opting for cost-effective components.

Progress:

  • Finalized and ordered all system components.
  • Worked on the design presentation. 
  • Began planning the software, including machine learning integration and the web application.

Next steps:

  • Set up the Raspberry Pi 5, sensors, and camera once the components arrive.
  • Finalize the data collection pipeline for ML training.
  • Develop the web app framework and user interface.

We will include photos of the system setup once the components arrive.

 

Part A: Our automated greenhouse system enhances public health, safety, and welfare by providing an efficient, low-maintenance way to grow fresh produce, improving food security and reducing reliance on commercially farmed crops with chemical treatments. Safety is a top priority, and we are implementing physical dividers to separate water-sensitive electrical components from wet areas like the water tank, minimizing the risk of short circuits or electrical hazards. Additionally, the system automates irrigation and nutrient delivery, preventing overwatering, mold growth, and manual handling risks. With live monitoring and an intuitive control interface, the system reduces the need for constant supervision, making it safer and more accessible for users with limited mobility or time constraints. By offering an affordable and sustainable way to grow plants, our solution supports self-sufficiency and environmental sustainability while ensuring a safer, healthier growing environment.

Part B: In terms of social factors, our project is significant in that it promotes sustainable urban living by allowing easy indoor plant care. People who cannot be physically present at home to take care of the plants can take advantage of our solution. It is also especially impactful in communities that have limited access to green spaces, as people can grow their plants and form their personal gardens inside their houses. People that share the same sustainability goals regarding plant care can connect more, and educate each other with the plant information that our solution provides. Sproutly contributes to achieving more green-friendly and socially connected society.

Part C: Our goal is to create a mid-range product that balances affordability and advanced features. High-end competitors like AC Infinity ($699 for a 3 plant system) and Plant Hive ($984 for 1 plant) offer advanced sensing and automation but at a steep price. Our system provides similar capabilities at a more accessible cost of $385. This is comparable to Koru ($400), a lower-cost option that offers basic sensors and watering schedules but lacks control over external conditions. Unlike cheaper alternatives that support only one plant in an open environment, our system accommodates three or more plants in an enclosed space with real-time sensing, monitoring, camera feed, and ML-based plant health analysis. This makes our system a cost-effective alternative with premium features at a lower price point.

Part A was written by Zara, B was written by Yuna, and C was written by Jana.

 

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Jana’s Status Report for 02/15/2025

I researched and finalized the components required for the system, including all the sensors and the camera. I ensured that these components met the project’s durability and cost requirements and considered the pin requirements for connection to the Raspberry Pi 5.

I also worked on the design presentation, where I was responsible for updating the project schedule. Additionally, I researched the quantitative design requirements and linked them to the use case requirements wherever applicable.

I began researching potential plant health detection model datasets, including sensor and image datasets. While I found many datasets related to crop diseases and general health classification, I encountered a lack of suitable ones focused on household plants, specifically addressing conditions like over/under-watering, nutrient deficiencies, or excessive heat. Most datasets are centered on crop diseases or pest detection, which may not fully align with our needs.

I researched models that could be used for training the machine learning model, including pre-trained CNN models such as ResNet and MobileNet, as well as MLP models. I identified possible relevant works.

My progress is on schedule. I have completed the component research task and I am on track with my responsibilities for dataset research.

Next Week’s Deliverables:

  • Finalize dataset and model selection. If necessary, I will explore the option of training from my own dataset, though I am unsure if that is feasible.
  • Begin working on leaf detection using OpenCV.
  • Work on my assigned sections for the design report.
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Yuna’s Status Report for 02/15/2025

I looked up specific components to be ordered and made sure they met our project’s requirements. I specifically finalized actuator components like mini heater, humidifier, and LED lights.

For design presentation, I worked on listing solution approaches for hardware and software and testing and verification metrics.

I finished the initial setup with Django and React frameworks and made sure they work together correctly. Our original plan on the schedule was to first finish the backend portion and work on frontend later. However, while I was setting up the code I realized working on them together would be more efficient. I wrote basic code that can store plant database in Django. Currently, the database is based on sqlLite, but I’ll later use AWS DynamoDB to store data.

I did research about how I can use AWS Educate as a student. I applied to AWS Educate and got registered to the program. According to the website description, a student will get a credit of $35 in AWS Educate, which I believe will be enough for our project.

I am currently on schedule.

Next week, I will start implementing API calls for plant database and confirm API is being called correctly. I will also start working on basic UI of the app  (frontend).