Milan Sulc presents Fine-grained recognition of plants from images

Fine-grained recognition of plants from images is a challenging computer vision
task, due to the diverse appearance and complex structure of plants, high
intra-class variability and small inter-class differences. We review the
state-of-the-art and discuss plant recognition tasks, from identification of
plants from specific plant organs to general plant recognition “in the
wild”.
We propose texture analysis and deep learning methods for different plant
recognition tasks. The methods are evaluated and compared them to the
state-of-the-art. Texture analysis is only applied to images with unambiguous
segmentation (bark and leaf recognition), whereas CNNs are only applied when
sufficiently large datasets are available. The results provide an insight in
the
complexity of different plant recognition tasks. The proposed methods
outperform
the state-of-the-art in leaf and bark classification and achieve very
competitive results in plant recognition “in the wild”.
The results suggest that recognition of segmented leaves is practically a
solved
problem, when high volumes of training data are available. The generality and
higher capacity of state-of-the-art CNNs makes them suitable for plant
recognition “in the wild” where the views on plant organs or plants vary
significantly and the difficulty is increased by occlusions and background
clutter.