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Article
Interpretable Deep Learning applied to Plant Stress Phenotyping
arxiv
  • Sambuddha Ghosal, Iowa State University
  • David Blystone, Iowa State University
  • Asheesh K. Singh, Iowa State University
  • Baskar Ganapathysubramanian, Iowa State University
  • Arti Singh, Iowa State University
  • Soumik Sarkar, Iowa State University
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
10-28-2017
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.

Comments

This is a pre-print made available through arxiv: https://arxiv.org/abs/1710.08619.

Copyright Owner
The Authors
Language
en
File Format
application/pdf
Citation Information
Sambuddha Ghosal, David Blystone, Asheesh K. Singh, Baskar Ganapathysubramanian, et al.. "Interpretable Deep Learning applied to Plant Stress Phenotyping" arxiv (2017)
Available at: http://works.bepress.com/baskar-ganapathysubramanian/70/