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Tomato Classification using DL

Shak Morag,Eitan K

Supervised by Alon Zvirin


Diagnosis of crop phenotypes is a widespread problem that affects the life of millions around the world. In our project, our goal was to create a system for classifying different parts of images in order to achieve classification and segmentation of significant plant parts. Our methods included applying neural networks. We've tested a few types of networks, mainly classifier and encoder-decoder. Using the classifier, we achieved very high accuracy (97%- 98%) when classifying parts of the image. Later, using the encoder-decoder architecture we shortened the required time for segmenting an image, from minutes to a few seconds.

Project Tomato Classification using DL Picture 1
Project Report

Please, see project report.

Final Presentation

Please, see final presentation.


Please, see Code.


Please, see demo clip.

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