{"id":19445485,"url":"https://github.com/mjmolina/plantaris_data","last_synced_at":"2026-03-03T18:31:54.538Z","repository":{"id":154285140,"uuid":"291360831","full_name":"mjmolina/plantaris_data","owner":"mjmolina","description":"Help your plants to stay healthier with Machine Learning at home.","archived":false,"fork":false,"pushed_at":"2024-06-25T01:35:44.000Z","size":1771,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-10-11T23:22:43.636Z","etag":null,"topics":["circuitpython","data-science","iot","machine-learning","machine-learning-algorithms","neural-network","plants","raspberry-pi","watering-plants"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mjmolina.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-08-29T22:46:01.000Z","updated_at":"2024-06-25T01:36:07.000Z","dependencies_parsed_at":null,"dependency_job_id":"d9b47431-4f1c-4dd9-a828-e96d87d91c20","html_url":"https://github.com/mjmolina/plantaris_data","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mjmolina/plantaris_data","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjmolina%2Fplantaris_data","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjmolina%2Fplantaris_data/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjmolina%2Fplantaris_data/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjmolina%2Fplantaris_data/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mjmolina","download_url":"https://codeload.github.com/mjmolina/plantaris_data/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjmolina%2Fplantaris_data/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30054585,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-03T18:21:05.932Z","status":"ssl_error","status_checked_at":"2026-03-03T18:20:59.341Z","response_time":61,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["circuitpython","data-science","iot","machine-learning","machine-learning-algorithms","neural-network","plants","raspberry-pi","watering-plants"],"created_at":"2024-11-10T16:10:45.054Z","updated_at":"2026-03-03T18:31:54.512Z","avatar_url":"https://github.com/mjmolina.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# plantaris_data\n## Category: Data Science Applications Talk\n\n## **Help your plants to stay healthier with Machine Learning at home**\n\n\n\u003cimg align=\"right\" width=\"350\" height=\"350\" src=\"img/plantaris_data.png\"/\u003e\n\n\n## Talk description\nMany studies have also proven that growing indoor house plants, as long as\nbeing a trend, improves health, despite the difficulty to keep them alive.  The\nfact is that when we grow plants inside our homes, they depend 100% on us and\nsometimes it is difficult to know what they need.\n\nIn this talk, we will explore how to improve your plants' lives by setting up\na basic plant monitoring system. This might sound complicated, but it is indeed\nvery simple and useful: thanks to using python and jupyter notebooks. Moreover,\nyou are going to create a machine learning pipeline from going through the\nsteps of data labeling, selecting the framework, the model, and you will learn\nhow to deal with the challenges that one can have in these processes.\n\nFinally, you will see how this project is an excellent way to learn how to deal\nwith data science challenges, at the same time that you will learn plant\nbiology and how to implement a real-world machine learning project, and at the\nsame time help your plant to be happier.\n\n* You can watch the talk [here](https://www.youtube.com/watch?v=S9LbxLDW7ig\u0026t=131s)\n\n## Audience\n(1) This talk is beginner friendly. The main idea of this talk is to show with\na practical example how we can implement machine learning at home, at the same\ntime, how we can store and work with our own data for analysis.\n\n(2) The background knowledge should be basic python knowledge. This proposal of\ntalk is complementary to a previous personal project called \"PLANTARIS\"\n(https://github.com/mjmolina/plantaris).\n\n## Project structure\n\n```\n.\n├── boards\n│   ├── circuit_moisture.py\n│   ├── circuit_watering_simple.py\n│   ├── pi_monitor.py\n│   └── requirements.txt\n│ \n└── notebooks\n    ├── circuitpython_notebook.ipynb\n    ├── data_analysis_sensors.ipynb\n    ├── data_labeling.ipynb\n    ├── prediction.ipynb\n    ├── requirements.txt\n    ├── temp_hum_processed.csv\n    └── training.ipynb\n```\n\n### boards\n\nThis contains the scripts used to monitor the system:\n\n| File                  | Platform       | Description                        |\n| :-------------------- |:-------------- | :--------------------------------- |\n| `circuit_moisture.py` | CPX+Crickit    | Needs to be called `code.py`.      |\n|                       |                | Get the moisture every `60s`       |\n|                       |                | so the RPI can read it.            |\n| `pi_monitor.py`       | Raspberry Pi   | Listen to the CPX+Crickit system   |\n|                       |                | via Serial Port. Additionally      |\n|                       |                | it takes the environmental         |\n|                       |                | temperature and humidity.          |\n|                       |                | Generates the `temp_hum.csv` file. |\n\n`circuit_watering_simple.py` is an example from the talk of a system,\nthat is in charge of watering a plant activating a relay that enables a water\npump, according to the moisture sensor values.\n\n### notebooks\n\nJupyter Notebooks to perform different steps on this experiments:\n* `data_labeling.ipynb`, system based on Jupyter Widgets to do an interactive\n  labeling of all the photos that the system is taking.\n* `prediction.ipynb`, ML prediction step, more comments can be found inside.\n* `training.ipynb`, ML training step, more comments can be found inside.\n* `circuitpython_notebook.ipynb`, configuration to use the CPX directly from\n  a Notebook.\n* `data_analysis_sensors.ipynb`, analysis of all the data gathered by the\n  system.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmjmolina%2Fplantaris_data","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmjmolina%2Fplantaris_data","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmjmolina%2Fplantaris_data/lists"}