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Paddy Rice Imagery Dataset for Panicle Segmentation

文献类型： 外文期刊

作者： Wang, Hao 1 ; Lyu, Suxing 2 ; Ren, Yaxin 3 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

2.Hokkaido Univ, Grad Sch Agr, Sapporo, Hokkaido 0658589, Japan

3.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： image segmentation; panicle detection; deep learning; smart agriculture; unmanned aerial vehicle platform

期刊名称：AGRONOMY-BASEL （ 影响因子：3.417； 五年影响因子：3.64 ）

ISSN：

年卷期： 2021 年 11 卷 8 期

页码：

收录情况： SCI

摘要： Accurate panicle identification is a key step in rice-field phenotyping. Deep learning methods based on high-spatial-resolution images provide a high-throughput and accurate solution of panicle segmentation. Panicle segmentation tasks require costly annotations to train an accurate and robust deep learning model. However, few public datasets are available for rice-panicle phenotyping. We present a semi-supervised deep learning model training process, which greatly assists the annotation and refinement of training datasets. The model learns the panicle features with limited annotations and localizes more positive samples in the datasets, without further interaction. After the dataset refinement, the number of annotations increased by 40.6%. In addition, we trained and tested modern deep learning models to show how the dataset is beneficial to both detection and segmentation tasks. Results of our comparison experiments can inspire others in dataset preparation and model selection.

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