{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:54:38Z","timestamp":1782280478820,"version":"3.54.5"},"reference-count":69,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019R1A6A1A09031717"],"award-info":[{"award-number":["2019R1A6A1A09031717"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Plant diseases must be identified at the earliest stage for pursuing appropriate treatment procedures and reducing economic and quality losses. There is an indispensable need for low-cost and highly accurate approaches for diagnosing plant diseases. Deep neural networks have achieved state-of-the-art performance in numerous aspects of human life including the agriculture sector. The current state of the literature indicates that there are a limited number of datasets available for autonomous strawberry disease and pest detection that allow fine-grained instance segmentation. To this end, we introduce a novel dataset comprised of 2500 images of seven kinds of strawberry diseases, which allows developing deep learning-based autonomous detection systems to segment strawberry diseases under complex background conditions. As a baseline for future works, we propose a model based on the Mask R-CNN architecture that effectively performs instance segmentation for these seven diseases. We use a ResNet backbone along with following a systematic approach to data augmentation that allows for segmentation of the target diseases under complex environmental conditions, achieving a final mean average precision of 82.43%.<\/jats:p>","DOI":"10.3390\/s21196565","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T21:37:49Z","timestamp":1633901869000},"page":"6565","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":123,"title":["An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0238-0505","authenticated-orcid":false,"given":"Usman","family":"Afzaal","sequence":"first","affiliation":[{"name":"Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7014-4868","authenticated-orcid":false,"given":"Bhuwan","family":"Bhattarai","sequence":"additional","affiliation":[{"name":"Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9842-8704","authenticated-orcid":false,"given":"Yagya Raj","family":"Pandeya","sequence":"additional","affiliation":[{"name":"Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joonwhoan","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.3389\/fpls.2018.01162","article-title":"High-performance deep neural network-based tomato plant diseases and pests diagnosis system with refinement filter bank","volume":"9","author":"Fuentes","year":"2018","journal-title":"Front. 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