Scoring Root Necrosis in Cassava Using Semantic Segmentation
This provides a quantitative measure for necrosis scoring in cassava roots, aiding breeders in Africa, but it is incremental as it applies an existing method (UNet) to a new agricultural dataset.
The paper tackled the problem of subjective visual inspection for scoring root necrosis in cassava affected by Cassava Brown Streak Disease by automating the process using deep convolutional neural networks with semantic segmentation, achieving a mean Intersection over Union (IoU) of 0.90 on the test set.
Cassava a major food crop in many parts of Africa, has majorly been affected by Cassava Brown Streak Disease (CBSD). The disease affects tuberous roots and presents symptoms that include a yellow/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 convolutional neural networks with semantic segmentation. Our experiments show that the UNet model performs this task with high accuracy achieving 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 scoring 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.