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U.S.A."],"published-print":{"date-parts":[[2018,5]]},"abstract":"<jats:title>Significance<\/jats:title>\n          <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>","DOI":"10.1073\/pnas.1716999115","type":"journal-article","created":{"date-parts":[[2018,4,17]],"date-time":"2018-04-17T15:02:02Z","timestamp":1523977322000},"page":"4613-4618","update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":471,"title":["An explainable deep machine vision framework for plant stress phenotyping"],"prefix":"10.1073","volume":"115","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9424-5655","authenticated-orcid":false,"given":"Sambuddha","family":"Ghosal","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Iowa State University, Ames, IA 50011;"}]},{"given":"David","family":"Blystone","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Iowa State University, Ames, IA 50011"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7522-037X","authenticated-orcid":false,"given":"Asheesh K.","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Iowa State University, Ames, IA 50011"}]},{"given":"Baskar","family":"Ganapathysubramanian","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Iowa State University, Ames, IA 50011;"}]},{"given":"Arti","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Iowa State University, Ames, IA 50011"}]},{"given":"Soumik","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Iowa State University, Ames, IA 50011;"}]}],"member":"341","published-online":{"date-parts":[[2018,4,16]]},"reference":[{"key":"e_1_3_4_1_2","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/07352681003617285","article-title":"Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging","volume":"29","author":"Bock C","year":"2010","unstructured":"C Bock, G Poole, P Parker, T Gottwald, Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. 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