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Watermelon fruits detection using deep learning.

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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, 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 is one of the best in this area, so I sticked with it.

Check this Colab notebook 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

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, ...