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Team Status Report for 03/08/2025

No major changes were made to our system though we have decided to add a pid control system to our environment to control the actuators based on sensor data. 

Challenges & Mitigation:

  • Potential faulty sensors: If the sensors we have received are faulty we need to order backups which will increase our costs. We have managed to decrease costs in other areas to have more budget for potential backups. 
  • Not enough training data: An ongoing issue remains that we may not be able to collect enough plant data to sufficiently train the ML model, to combat this we have ordered the plants to start training. 

Progress:

  • Collected all the plants for training and testing, and started setting them up in different conditions for ML training
  • Finished setting up the temperature and humidity sensor, and started setting up the light sensor
  • Created a rough web application with all base pages
  • Labeled online datasets consistently for ML models

Next Steps:

  • Finish setting light and soil moisture, pH, and, nutrients sensor
  • Establish a training framework for the ML model
  • Implement web UI, and django channels with WebSockets
  • Integrate RaspberryPi with web app

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

Part A: Our product addresses global factors by recognizing that plant care is a universal activity, with people worldwide facing different challenges in managing plants due to varying levels of expertise, time, and environmental conditions. This product bridges the gap for individuals who lack the time or knowledge to care for plants, offering a solution for growing herbs, fruits, vegetables, and flowers at home, regardless of experience. It is particularly valuable in remote areas or regions with limited access to plant care resources, providing an educational tool for users at all skill levels. Additionally, its remote monitoring capabilities support plant care in diverse global settings, making it a versatile solution for anyone looking to grow plants efficiently, no matter where they are located.

Part B: In many cultures, plants hold symbolic and spiritual significance. Our system offers personalized plant care recommendations that meet the specific needs of plants that hold cultural significance, such as herbs, flowers, and medicinal plants. The system is designed to adapt to a wide range of growing conditions, ensuring that users from diverse cultural backgrounds, regardless of level of expertise or traditions, can effectively use the technology. By providing a simple and intuitive way to manage plant health, users can stay connected to the plants that are important to them, while also benefiting from automation and helpful insights that make plant care easier.

Part C: Our product solution addresses environmental challenges by allowing optimization of resource consumption for plant cultivation in home settings. The greenhouse system reduces energy use and water waste through precise irrigation that only delivers the water and nutrient amount that is needed for the plant. The system detects the plant conditions and water the appropriate amount rather than merely watering based on the typical watering schedules – this is particularly valuable in regions that are facing water scarcity. This plant condition detection also helps prevent overfertilization that can lead to soil degradation. Furthermore, our product solution allows easy plant cultivation in limited spaces, which improves air quality and reduces the carbon emissions caused by transporting commercial produce.

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Zara’s Status Report for 03/08/2025

This week, I helped work on the design status report, which has helped further solidify our project plans. I focused on reworking the system architecture, specifically the flow chart diagram, to incorporate the new decision to use a PID controller for managing the system’s environment. Additionally, I updated the system implementation plan, bill of materials, and prepared a summary to align our project’s direction moving forward. On the sensor front, I conducted tests on various temperature and humidity sensors to identify the most accurate one, replacing the initially tested model. I also began implementing the light sensor into our system. For the plant API call, I am still in the process of finalizing the code to ensure smooth integration and functionality.

In terms of schedule, I am mostly on track; however, I have encountered a delay in implementing the heater. This is due to shipping issues, as the heater has yet to arrive, which is preventing me from progressing as planned

Looking ahead, my deliverables for next week include completing the implementation of the soil nutrients, pH, and moisture sensors, as well as finalizing the light sensor integration. If the heater arrives as expected, I aim to complete its implementation as well.

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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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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 02/08/2025

One of the most significant risks to our project is ensuring that we have enough time to train a machine learning model on sensor input data. If we do not set up our data collection system early, we may not have sufficient training data, which could result in an underdeveloped model that does not perform as expected. To mitigate this risk, our approach is to prioritize setting up the fundamental hardware and software components as soon as possible. This includes setting up the Raspberry Pi 5 with an initial sensor and the camera module, establishing a data collection and storage system to begin logging sensor and camera input, and implementing the initial machine learning pipeline so we can start training models early in the development process. By doing this, we ensure that data collection and ML training can happen concurrently with other parts of the project, minimizing delays.

There were no major changes to the design of the system. However, we made one key adjustment by switching from the Raspberry Pi 4B to the Raspberry Pi 5. This decision was made because the Raspberry Pi 5 offers better performance, which will help with machine learning training and real-time processing. Additionally, we were able to secure one for free from the ECE inventory, reducing project costs. This change does not introduce additional expenses but improves our system’s capability and allows us to work with newer hardware.

At this point, there are no major schedule updates. Our current focus is on ordering components and beginning initial setup. Once our parts arrive, we will start hardware integration and software development.

So far, we have finalized our project proposal and user case requirements, researched communication protocols for sensor and camera data transfer, ordered our first Raspberry Pi 5 unit, and finalized the initial list of parts to order next week. Our next steps include ordering all remaining parts and beginning hardware setup, setting up the Raspberry Pi 5 with essential sensors and the camera, and establishing the data collection and machine learning training pipeline. We will include photos of our hardware setup in future updates as we begin assembling components.

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Zara’s Status Report for 02/08/2025

This week, I collaborated with my team to finalize the proposal presentation slides. This included refining our user case requirements and engaging in an in-depth discussion on testing methodologies for different project components. We ensured that each aspect of our design was well-justified and accounted for potential testing strategies.

Additionally, I conducted further research into the communication protocols we will be using between the Raspberry Pi (RPI) and our Django-based web application. This involved understanding how we will handle data transmission for the sensors and the camera module.

To ensure we received the necessary hardware on time, I placed an early order for our first Raspberry Pi 5 unit. This will allow us to begin setup as soon as possible and secure the hardware before free units run out from inventory. As a team, we also finalized the list of initial components we plan to order for the base system.

Our project is currently on track. The planning phase is progressing well, and we expect to move into the implementation phase soon.

Our goals for next week include finalizing and placing the order for all remaining required components, specifically, the sensors we want to start on including temperature and light. We also want to begin setting up and testing the Raspberry Pi 5 to ensure compatibility with our planned system architecture.