4.5 Article

Wheat Ear Segmentation Based on a Multisensor System and Superpixel Classification

期刊

PLANT PHENOMICS
卷 2022, 期 -, 页码 -

出版社

AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.34133/2022/9841985

关键词

-

资金

  1. Agriculture, Natural Resources and Environment Research Direction of the Public Service of Wallonia (Belgium) [D31-1385 PHENWHEAT]
  2. National Fund of Belgium Fonds de la Recherche Scientifique -FNRS (FRIA grant)

向作者/读者索取更多资源

This paper proposes an easy and robust method to segment wheat ears from heading to maturity growth stage. The method is based on superpixel classification using features from RGB and multispectral cameras. The results show variations in the segmentation of ears based on different growth stages and nitrogen fertilizer levels.
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 support vector machine (SVM), yielded satisfactory segmentation and reached 94% accuracy. However, the segmentation at the pixel level could not be assessed only by the superpixel classification accuracy. For this reason, a second assessment method was proposed to consider the entire process. A simple graphical tool was developed to annotate pixels. The strategy was to annotate a few pixels per image to be able to quickly annotate the entire image set, and thus account for very diverse conditions. Results showed a lesser segmentation score (F1-score) for the heading and flowering stages and for the zero nitrogen input object. The methodology appeared appropriate for further work on the growth dynamics of the different wheat organs and in the frame of other segmentation challenges.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据