Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images.

ABSTRACT: Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots are usually done manually, or semi-automatically by first segmenting cassava root images. However, occlusion of both storage and fibrous roots makes the process both time-consuming and error-prone. While Convolutional Neural Nets have shown performance above the state-of-the-art in many image processing and analysis tasks, there are currently a limited number of Convolutional Neural Net-based methods for counting plant features. This is due to the limited availability of data, annotated by expert plant biologists, which represents all possible m

SUBMITTER: Atanbori J 

PROVIDER: S-EPMC6888701 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature