{"id":82,"date":"2025-02-15T20:52:17","date_gmt":"2025-02-15T20:52:17","guid":{"rendered":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s25-teamb3\/?p=82"},"modified":"2025-02-15T20:54:12","modified_gmt":"2025-02-15T20:54:12","slug":"janas-status-report-for-02-15-2025","status":"publish","type":"post","link":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s25-teamb3\/2025\/02\/15\/janas-status-report-for-02-15-2025\/","title":{"rendered":"Jana\u2019s Status Report for 02\/15\/2025"},"content":{"rendered":"

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.<\/span><\/p>\n

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.<\/span>
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\n<\/span>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.<\/span><\/p>\n

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.<\/span>
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\n<\/span>My progress is on schedule. I have completed the component research task and I am on track with my responsibilities for dataset research.<\/span><\/p>\n

Next Week\u2019s Deliverables:<\/span><\/p>\n