https://github.com/alkasaliss/pastai
Watermelon fruits detection using deep learning.
https://github.com/alkasaliss/pastai
agriculture-data agrotech computer-vision cvat machine-learning pytorch yolov5
Last synced: 3 months ago
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Watermelon fruits detection using deep learning.
- Host: GitHub
- URL: https://github.com/alkasaliss/pastai
- Owner: AlkaSaliss
- Created: 2021-05-27T21:43:55.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2021-05-28T20:59:21.000Z (about 4 years ago)
- Last Synced: 2025-01-19T17:54:44.444Z (4 months ago)
- Topics: agriculture-data, agrotech, computer-vision, cvat, machine-learning, pytorch, yolov5
- Language: Jupyter Notebook
- Homepage:
- Size: 61.5 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
**True story!**
A friend of mine (let's call him Carmelo, coz it sounds like watermelon) is ruling a small garden with watermelon plants (digression: I love eating watermelon 😸). However, it wouldn't be a good scenario if everything was oki koki :
* Watering and putting fertilizer is inefficient, because it doesn't target the right places where water and fertilizer are needed, and these resources are expensive
* In early stage, the fruits are vulnerable to insects attacks :
* When the fruits reached maturity, they receive the visit of unexpected watermelon lovers, the crows, see results of their lovely visits :
I thought for a while and came with (the not so special) idea that using machine learning + IoT can help in most of the above problems. For example an object detection computer vision model can help in :
* More effectively target areas that need water/fertilizer
* Detect/count attacked fruits
* Detect presence of attackers and ring the alarm bell
* Count/estimate number of fruit in a given area
* ...
First step in data science project is problem definition. Here I wanted to start simple and build a simple app (web, mobile ? I dunno yet ¯\\_(ツ)_/¯) around the watermelon detection in the wild.
After problem definition, next logical step is to acquire data. To spice things up, I decided to create and label a small dataset. After all:
So, Carmelo (remember him ?) recorded a short video of the plants, of about two and half minutes. After a little investigation about opensource image labelling tools, I found [CVAT](https://github.com/openvinotoolkit/cvat), a tool by Intel, to be the right one for me in terms of :
* easy to install: through simple a docker-compose service
* intuitive : it offers a simple web interface for labelling and also for labellers accounts administation
* functionalities : the main ones used in computer vision such as segmentation masks, key points, bounding boxes, ...
After about an equivalent of a day of working, I managed ot label around 4.7K images with bounding boxes :
> Labelling is an exhausting, task, and I needed to go back to some images multiple times to adjust the boxes.
Data being created, I searched for a good object detection model that offers a good trade-off between accuracy and speed, as I may want to deploy the model on mobile/edge devices later. [Yolo V5](https://github.com/ultralytics/yolov5) is one of the best in this area, so I sticked with it.
Check this [Colab notebook](https://colab.research.google.com/github/AlkaSaliss/Pastai/blob/master/notebooks/YOLO_v5_training.ipynb) to see how to train diffent, Yolo v5 models end-to-end, from data download until model evaluation and conversion.
The trained model is deployed on streamlit and can be accessed through [this link](https://share.streamlit.io/alkasaliss/pastai/src/pastai_app.py)
Next steps :
* Label new data to:
* detect attacked watermelon
* create a segmentation map around the attacked area
* count number of healthy/attacked fruits in a given field area
* Find a way to make the models useful for our friend Carmelo, for example by embedding the app on mobile/Raspberry Pi camera, ...