An explainable deep machine vision framework for plant stress phenotyping

Abstract

Significance Plant stress identification based on visual symptoms has predominately remained a manual exercise performed by trained pathologists, primarily due to the occurrence of confounding symptoms. However, the manual rating process is tedious, is time-consuming, and suffers from inter- and intrarater variabilities. Our work resolves such issues via the concept of explainable deep machine learning to automate the process of plant stress identification, classification, and quantification. We construct a very accurate model that can not only deliver trained pathologist-level performance but can also explain which visual symptoms are used to make predictions. We demonstrate that our method is applicable to a large variety of biotic and abiotic stresses and is transferable to other imaging conditions and plants.

Code and data availability

The paper's soybean stress leaf image dataset (25,000+ labeled images) and trained DCNN model are explicitly deposited on the authors' public GitHub repository, stated in both the Methods and Data deposition footnote. The Plant and Insect Diagnostic Clinic URL is an external service reference, not a paper-specific data