  In this paper, a 1d convolutional neural network is designed for
classification tasks of plant leaves. This network based classifier is analyzed
in two directions. In the forward direction, the proposed network can be used
in two ways: a classifier and an automatic feature extractor. As a classifier,
it takes the simple centroid contour distance curve as the single feature and
achieves comparable performance with state-of-art methods that usually require
multiple extracted features. As a feature extractor, it produces nearly linear
separable features, hence can be used together with other classifiers such as
support vector machines to provide better performance. The proposed network
adopts simple 1d input and is generally applicable for other tasks such as
classifying one dimensional time series in an end-to-end fashion without
changes. Experiments on some benchmark datasets show this architecture can
provide classification accuracies that are comparable or higher than some
existing methods. In the backward direction, methods like gradient-weighted
class activation mapping and maximum activation map of neurons in the
classification layer with respect to inputs are performed to help investigate
and further validate that hidden signatures helping trigger the trained
classifier's specific decisions can be human interpretable. Code for the paper
is available at https://github.com/dykuang/Leaf_Project.
