Paddy Rice Imagery Dataset for Panicle Segmentation

Abstract

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.

Code and data availability

The paper's pixel-level annotated paddy rice panicle imagery dataset (400 4K UAV images, 50,730 annotations) is explicitly stated to be publicly available on Zenodo via DOI 10.5281/zenodo.4430186, a paper-specific public asset directly reproducing the phenotyping measurements.