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Mobiusi/Corn-Seedling-Recognition-Dataset

Corn Seedling Recognition Dataset The current challenge in the agriculture industry is the precision and inefficiency of crop growth monitoring. Traditional methods often rely on manual observation, which is inefficient and prone to errors. Existing solutions mostly involve manual annotation, lacking standardization and consistency. This dataset aims to promote automated monitoring technology in the agricultural field by building a high-quality corn seedling recognition dataset.… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Corn-Seedling-Recognition-Dataset.

sourceHugging Facecc-by-nc-sa-4.0updated 7mo agoView on Hugging Face

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Dataset Card

Corn Seedling Recognition Dataset

The current challenge in the agriculture industry is the precision and inefficiency of crop growth monitoring. Traditional methods often rely on manual observation, which is inefficient and prone to errors. Existing solutions mostly involve manual annotation, lacking standardization and consistency. This dataset aims to promote automated monitoring technology in the agricultural field by building a high-quality corn seedling recognition dataset. The dataset includes corn seedling images from different regions, captured using high-resolution cameras to ensure image clarity. During data collection, multiple rounds of annotation and expert review were adopted to ensure the quality of data labeling. The storage format is JPG, organized such that each image corresponds to an annotation file containing bounding boxes and category information.

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
plant_count	int	The number of corn seedlings present in the image.
averageplantheight	float	The average height of the corn seedlings in the image, measured in centimeters.
plant_health	string	The health status of each seedling marked as healthy, water deficient, pest/disease affected, etc.
soil_condition	string	The visible condition of the soil, such as moist, dry, weed-covered, etc.
lighting_condition	string	The lighting condition at the time the image was taken, such as sunny, cloudy, artificial light, etc.
weed_presence	boolean	Indicates whether weeds are present in the image.
background_elements	string	Visible background elements in the image, such as stones, tools, roads, etc.
plant_density	float	The density of plants in the image, expressed as the number of plants per square meter.

Compliance Statement

<table> <tr> <td>Authorization Type</td> <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td> </tr> <tr> <td>Commercial Use</td> <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td> </tr> <tr> <td>Privacy and Anonymization</td> <td>No PII, no real company names, simulated scenarios follow industry standards</td> </tr> <tr> <td>Compliance System</td> <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td> </tr> </table>

Source & Contact

If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com