An explainable deep machine vision framework for plant stress phenotyping
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- Sambuddha Ghosal
- Department of Mechanical Engineering, Iowa State University, Ames, IA 50011;
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- David Blystone
- Department of Agronomy, Iowa State University, Ames, IA 50011
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- Asheesh K. Singh
- Department of Agronomy, Iowa State University, Ames, IA 50011
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- Baskar Ganapathysubramanian
- Department of Mechanical Engineering, Iowa State University, Ames, IA 50011;
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- Arti Singh
- Department of Agronomy, Iowa State University, Ames, IA 50011
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- Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, IA 50011;
説明
<jats:title>Significance</jats:title> <jats:p>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.</jats:p>
収録刊行物
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- Proceedings of the National Academy of Sciences
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Proceedings of the National Academy of Sciences 115 (18), 4613-4618, 2018-04-16
Proceedings of the National Academy of Sciences