We propose using a deep convolutional neural network (CNN) for the problem of plant identification from leaf vein patterns. In particular we consider classifying three different legume species: white bean, red bean and soybean. The introduction of a CNN avoids using handcrafted feature extractors as in state of the art pipeline. Furthermore, this deep learning approach significantly improves the accuracy of the referred pipeline. We also show that this accuracy is reached by increasing the depth of the model. Finally, by analyzing the resulting models with a simple visualization technique, we are able to discover which vein patterns are relevant.
Grinblat, et al., proposes the use of CNN for the problem of identifying vein morphological vein of plants REF .
Deep learning for plant identification using vein morphological patterns
Comput. Electron. Agric.
Comput. Electron. Agric.
Engineering Computer Science