The task is to learn to classify such cuttings so that they can be planted with like sizes. There are two parts to this problem:
Segmentation of Images. The first step is to separate an image into a binarized image of the cutting. The next step is to segment the image into various parts -- the stem, leaf petioles and leaves.
Learning/Auto Classification. We are investigating various methods of teaching our system to classify plant cuttings. Typically, the learning method is presented with a list of features (from the segmentation above) and the class denoted by an expert grader.
Algorithms are validated by showing the algorithm an example and comparing the answer to the true classification. We use 10-fold cross validation for our tests. In our experiments, we have achieved over 90% accuracy in grading as compared to 75% by an expert human grader.
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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