Projects Last Projects
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.
Please, see project report.
Please, see final presentation.
Please, see Code.
Please, see demo clip.