Wheat Ear Segmentation Based on a Multisensor System and Superpixel Classification.

ABSTRACT: The automatic segmentation of ears in wheat canopy images is an important step to measure ear density or extract relevant plant traits separately for the different organs. Recent deep learning algorithms appear as promising tools to accurately detect ears in a wide diversity of conditions. However, they remain complicated to implement and necessitate a huge training database. This paper is aimed at proposing an easy and quick to train and robust alternative to segment wheat ears from heading to maturity growth stage. The tested method was based on superpixel classification exploiting features from RGB and multispectral cameras. Three classifiers were trained with wheat images acquired from heading to maturity on two cultivars at different levels of fertilizer. The best classifier, the supp

SUBMITTER: Carlier A 

PROVIDER: S-EPMC8817947 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature