DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field.

ABSTRACT:

Background

Flowering is one of the most important processes for flowering plants such as cotton, reflecting the transition from vegetative to reproductive growth and is of central importance to crop yield and adaptability. Conventionally, categorical scoring systems have been widely used to study flowering patterns, which are laborious and subjective to apply. The goal of this study was to develop a deep learning-based approach to characterize flowering patterns for cotton plants that flower progressively over several weeks, with flowers distributed across much of the plant.

Results

A ground mobile system (GPhenoVision) was modified with a multi-view color imaging module, to acquire images of a plant from four viewing angles at a time. A total of 116 plants from 23 genotypes

SUBMITTER: Jiang Y 

PROVIDER: S-EPMC7720604 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature