Analysis of Learned Features and Framework for Potato Disease Detection
Shikha Gupta Soma Chakraborty Renu Rameshan
For applications like plant disease detection, usually, a model is trained on
publicly available data and tested on field data. This means that the test data
distribution is not the same as the training data distribution, which affects
the classifier performance adversely. We handle this dataset shift by ensuring
that the features are learned from disease spots in the leaf or healthy
regions, as applicable. This is achieved using a faster Region-based
convolutional neural network (RCNN) as one of the solutions and an
attention-based network as the other. The average classification accuracies of
these classifiers are approximately 95% while evaluated on the test set
corresponding to their training dataset. These classifiers also performed
equivalently, with an average score of 84% on a dataset not seen during the
training phase.