Scoring Root Necrosis in Cassava Using Semantic Segmentation

dc.contributor.authorTusubira, Jeremy Francis
dc.contributor.authorAkera, Benjamin
dc.contributor.authorNsumba, Solomon
dc.contributor.authorNakatumba-Nabende, Joyce
dc.contributor.authorMwebaze, Ernest
dc.date.accessioned2022-12-29T14:01:02Z
dc.date.available2022-12-29T14:01:02Z
dc.date.issued2020
dc.description.abstractCassava a major food crop in many parts of Africa, has ma- jorly been a ected by Cassava Brown Streak Disease (CBSD). The dis- ease a ects tuberous roots and presents symptoms that include a yel- low/brown, dry, corky necrosis within the starch-bearing tissues. Cassava breeders currently depend on visual inspection to score necrosis in roots based on a qualitative score which is quite subjective. In this paper we present an approach to automate root necrosis scoring using deep convo- lutional neural networks with semantic segmentation. Our experiments show that the UNet model performs this task with high accuracy achiev- ing a mean Intersection over Union (IoU) of 0.90 on the test set. This method provides a means to use a quantitative measure for necrosis scor- ing on root cross-sections. This is done by segmentation and classifying the necrotized and non-necrotized pixels of cassava root cross-sections without any additional feature engineering.en_US
dc.identifier.citationTusubira, J. F., Akera, B., Nsumba, S., Nakatumba-Nabende, J., & Mwebaze, E. (2020). Scoring root necrosis in cassava using semantic segmentation. arXiv preprint arXiv:2005.03367.en_US
dc.identifier.urihttps://arxiv.org/abs/2005.03367
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/6748
dc.language.isoenen_US
dc.publisherarXiv preprint arXiven_US
dc.subjectCassavaen_US
dc.subjectCBSDen_US
dc.subjectNecrosisen_US
dc.subjectUNeten_US
dc.subjectSemantic segmentationen_US
dc.titleScoring Root Necrosis in Cassava Using Semantic Segmentationen_US
dc.typeArticleen_US
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