https://github.com/mr-talhailyas/precision-agriculture-datasets

A repository containing links of datasets used for training deep CNNs in precision agriculture.
https://github.com/mr-talhailyas/precision-agriculture-datasets

crops-and-weeds dataset deep-learning precision-agriculture

Last synced: 4 months ago
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A repository containing links of datasets used for training deep CNNs in precision agriculture.

• Host: GitHub
• URL: https://github.com/mr-talhailyas/precision-agriculture-datasets
• Owner: Mr-TalhaIlyas
• Created: 2022-09-07T06:26:45.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-11-28T08:20:09.000Z (almost 3 years ago)
• Last Synced: 2025-07-02T20:08:02.467Z (4 months ago)
• Topics: crops-and-weeds, dataset, deep-learning, precision-agriculture
• Homepage:
• Size: 7.81 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
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# Datasets

### Download Link [One Drive](https://o365jbnu-my.sharepoint.com/:f:/g/personal/talha_student_jbnu_ac_kr/EtY7EzFK6LhEq2ibaVmK_V4BP712j8fZNUuz7DaqiZGFlA?e=QMWVhH)

The above Onedrive link contains the links to all the datasets used in below papers.

### SS1K Dataset

[Paper](https://www.frontiersin.org/articles/10.3389/fpls.2021.591333/full)

### Strawberry Disease Dataset

[Paper]()

### Strawberry Crown Dataset

[Paper]()

### Strawberry Center Point Dataset

[Paper]()

# Rice and Paddy Dataset

[Paper]()

### Citation

```

Ilyas, T., Khan, A., Umraiz, M., Jeong, Y. and Kim, H., 2021. Multi-scale context aggregation for strawberry fruit recognition and disease phenotyping. IEEE Access, 9, pp.124491-124504.

Ilyas, T., Umraiz, M., Khan, A. and Kim, H., 2021. DAM: Hierarchical adaptive feature selection using convolution encoder decoder network for strawberry segmentation. Frontiers in plant science, 12, p.591333.

Ilyas, T. and Kim, H., 2021, October. A Deep Learning Based Approach for Strawberry Yield Prediction via Semantic Graphics. In 2021 21st International Conference on Control, Automation and Systems (ICCAS) (pp. 1835-1841). IEEE.

```