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Monsoon 2020: Completed #classroom Weight: 45.0

DA Project Plant

Classify leaves of the plant

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🛠 Contribute: Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here.

🕵️ Introduction

Only with the help of leaf you can talk to a forest. Most of the trees are identified by the type of leaves they have. Given a dataset consisting features of leaves, classify these leaves as a part of this multi class problem.

💾 Dataset

There are three features for each image: Shape, Margin and Texture. For each feature, a 64 element vector is given per leaf sample. These vectors are taken as a contiguous descriptor (for shape) or histograms (for texture and margin). Each row has a 64-element feature vector followed by the target variable Class label and it's value lies in the range 1-100 for 100 plants species.

📁 Files

Following files are available in the resources section:

  • train.csv - (1279 samples) File that should be used for training. It contains the feature representation and their respective outcomes.
  • test.csv - (320 samples) File that will be used for testing. Unlike the training file it contains only the feature representation and not their outcomes.

🚀 Submission

  • Prepare a python file which should produce a csv file with name "submission.csv" containing header as "class" and predicted value as digit between [1…100] representing one of the 100 possible classes.
  • Your submission should read the train and test data (available as environment variables) and should write 'submission.csv' containing the predictions for the test set.     
  • Sample submission format is available in the resources section of the challenge page as sample_submission.py.                            Make your first submission here 🚀 !!

🖊 Evaluation Criteria

During evaluation F1 score will be used to test the efficiency of the model where,

📱 Contact

  • Aditya Khandelwal

📚 References

  • Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

  • Author: James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman.

  • Charles Mallah, James Cope, James Orwell. Plant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features. Signal Processing, Pattern Recognition and Applications, in press. 2013.

  • Image Source

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