Skip to main content Skip to main navigation


Erosion Band Features for Cell Phone Image Based Plant Disease Classification

Marion Neumann; Lisa Hallau; Benjamin Klatt; Kristian Kersting; Christian Bauckhage
In: 22nd International Conference on Pattern Recognition. International Conference on Pattern Recognition (ICPR-2014), August 24-28, Stockholm, Sweden, Pages 3315-3320, IEEE Computer Society, 2014.


We introduce a novel set of features for a challenging image analysis task in agriculture where cell phone camera images of beet leaves are analyzed as to the presence of plant diseases. Aiming at minimal computational costs on the cellular device and highly accurate prediction results, we present an efficient detector of potential disease regions and a robust classification method based on texture features. We evaluate several first- and second-order statistical features for classifying textures of leaf spots and we find that a combination of descriptors derived on multiple erosion bands of the RGB color channels, as well as, the local binary patterns of gradient magnitudes of the extracted regions accurately distinguish between symptoms caused by five diseases, including infections of the fungi Cercospora beticola, Ramularia beticola, Uromyces betae, and Phoma betae, and the bacterium Pseudomonas syringae pv. aptata.

Weitere Links