# CongNaMul: A Dataset for Advanced Image Processing of Soybean Sprouts

arXiv: https://arxiv.org/abs/2308.15690 · HTML: https://mlsanity.com/paper/2308.15690/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 30 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications](https://mlsanity.com/paper/2011.07118/) (2020)
- [Machine learning approach to remove ion interference effect in agricultural nutrient solutions](https://mlsanity.com/paper/1907.10794/) (2019)
- [ODE network model for nonlinear and complex agricultural nutrient solution system](https://mlsanity.com/paper/1907.10800/) (2019)
- [Control of nonlinear, complex and black-boxed greenhouse system with reinforcement learning](https://mlsanity.com/paper/1907.12690/) (2019)
- [Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod](https://mlsanity.com/paper/2204.10902/) (2022)
- [NemaNet: A convolutional neural network model for identification of nematodes soybean crop in brazil](https://mlsanity.com/paper/2103.03717/) (2021)
- [Mixed Reality Interface for Digital Twin of Plant Factory](https://mlsanity.com/paper/2211.00597/) (2022)

## Built on by (most cited first, 1 of 1)

- [FAKER: Full-body Anonymization with Human Keypoint Extraction for Real-time Video Deidentification](https://mlsanity.com/paper/2408.11829/) (2024)

## Abstract (arXiv)

We present 'CongNaMul', a comprehensive dataset designed for various tasks in soybean sprouts image analysis. The CongNaMul dataset is curated to facilitate tasks such as image classification, semantic segmentation, decomposition, and measurement of length and weight. The classification task provides four classes to determine the quality of soybean sprouts: normal, broken, spotted, and broken and spotted, for the development of AI-aided automatic quality inspection technology. For semantic segmentation, images with varying complexity, from single sprout images to images with multiple sprouts, along with human-labelled mask images, are included. The label has 4 different classes: background, head, body, tail. The dataset also provides images and masks for the image decomposition task, including two separate sprout images and their combined form. Lastly, 5 physical features of sprouts (head length, body length, body thickness, tail length, weight) are provided for image-based measurement tasks. This dataset is expected to be a valuable resource for a wide range of research and applications in the advanced analysis of images of soybean sprouts. Also, we hope that this dataset can assist researchers studying classification, semantic segmentation, decomposition, and physical feature measurement in other industrial fields, in evaluating their models. The dataset is available at the authors' repository. (https://bhban.kr/data)