Scoring Root Necrosis in Cassava Using Semantic Segmentation
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
arXiv preprint arXiv
Abstract
Cassava 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.
Description
Keywords
Cassava, CBSD, Necrosis, UNet, Semantic segmentation
Citation
Tusubira, 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.