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Corn leaf segmentor - Phnomics

Adva Cohen and Shani Idgar

Supervised by Yaron Honen and Alon Zvirin


Analysis of maize leaves is a widespread issue, important for assessing plant growth. In our project our goals were to improve segmentation of maize leaves and to classify maize plants into two categories, untreated and fungi-infected, using our segmentation to create the dataset. Our methods to improve segmentation included a two-step inference process and improving the training by creating synthetic images. Our methods for classification included creating a Cifar-10 based CNN architecture model, trained from scratch. We demonstrate that creating a larger dataset using data augmentation and training the networks from scratch improves both segmentation and classification.

Project Corn leaf segmentor - Phnomics Picture 1
Project Corn leaf segmentor - Phnomics Picture 2
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