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tags:
  - object detection
  - image classification
  - agricultural monitoring
  - crop health assessment
  - precision agriculture
license: cc-by-nc-sa-4.0
task_categories:
  - object-detection
language:
  - en
pretty_name: Corn Seedling Recognition Dataset
size_categories:
  - 1B<n<10B

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.
average_plant_height 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

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

Source & Contact

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