Interpretable Deep Learning applied to Plant Stress Phenotyping

TL;DR

This paper presents an explainable deep learning framework for accurately identifying, classifying, and quantifying plant stresses, with an emphasis on soybean foliar stress detection and transferability across species.

Contribution

It introduces a machine learning model with an explanation mechanism for plant stress phenotyping, enabling rapid, objective, and transferable stress analysis.

Findings

High accuracy in stress classification in soybean

Effective use of gradient-weighted class activation mapping for explanations

Model shows transfer learning capabilities across species

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.