Title: Interpretable Deep Learning applied to Plant Stress Phenotyping,
Abstract: Availability of an explainable deep learning model that can be applied to
practical real world scenarios and in turn, can consistently, rapidly and
accurately identify specific and minute traits in applicable fields of
biological sciences, is scarce. Here we consider one such real world example
viz., accurate identification, classification and quantification of biotic and
abiotic stresses in crop research and production. Up until now, this has been
predominantly done manually by visual inspection and require specialized
training. However, such techniques are hindered by subjectivity resulting from
inter- and intra-rater cognitive variability. Here, we demonstrate the ability
of a machine learning framework to identify and classify a diverse set of
foliar stresses in the soybean plant with remarkable accuracy. We also present
an explanation mechanism using gradient-weighted class activation mapping that
isolates the visual symptoms used by the model to make predictions. This
unsupervised identification of unique visual symptoms for each stress provides
a quantitative measure of stress severity, allowing for identification,
classification and quantification in one framework. The learnt model appears to
be agnostic to species and make good predictions for other (non-soybean)
species, demonstrating an ability of transfer learning.