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Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification

Research datakeyboard_double_arrow_right Dataset 27 Jan 2023Publisher:Zenodo

Authors: Dong, David; Nagasubramanian, Koushik; Wang, Ruidong; Frei, Ursula; Jubery, Talukder Z.; Lubberstedt, Thomas; Ganapathysubramanian, Baskar;

doi: 10.5281/zenodo.7576661 , 10.5281/zenodo.7577017 , 10.5281/zenodo.7576660

Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification

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Abstract

These are companion data of manuscript "Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification" that was submitted to Frontiers in Plant Science. The data is organized into three main folders: 'class_full_imgs', 'seg_full_imgs', and 'unlabeled'. The 'class_full_imgs' folder contains labeled data used to train the classification model, which is divided into train, validation, and test subfolders. Each of these subfolders contains 'oriented' and 'non-oriented' images. The 'seg_full_imgs' folder contains labeled data used to train the segmentation model. The 'InputImages' subfolder contains raw images, and the 'OutputImages' subfolder contains the segmented images. The 'unlabeled' folder contains images without any labels. These images were used for self-supervised pretraining of classification and segmentation models.

This work was partially supported by the AI Institute for Resilient Agriculture (USDA-NIFA #2021-67021-35329), COALESCE: COntext Aware LEarning for Sustainable CybEr-Agricultural Systems (CPS Frontier # 1954556), and support from a PSI faculty fellowship.

Related Organizations

Iowa State University
United States

Keywords

Self-supervised training, Embryo, Maize kernel

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