Mobiusi's picture
initial commit
25583b8 verified
metadata
tags:
  - Image classification
  - deep learning training
  - pattern recognition
  - Agricultural automation
  - spraying operations
  - crop monitoring
license: cc-by-nc-sa-4.0
task_categories:
  - image-classification
language:
  - en
pretty_name: Pesticide Spraying Scene Classification Dataset
size_categories:
  - 10M<n<100M

Pesticide Spraying Scene Classification Dataset

The current agricultural industry faces challenges such as low spraying efficiency and environmental pollution, especially during large-scale farmland spraying. Traditional methods rely on manual operations, which can lead to pesticide waste and uneven spraying. Existing solutions often lack efficient image recognition technology and cannot monitor spraying effectiveness and crop conditions in real-time. This dataset aims to support the intelligent development of agriculture by providing high-quality images of spraying scenes, thereby improving the efficiency and accuracy of spraying operations. Data collection uses high-resolution cameras in actual spraying environments to ensure images accurately reflect operational conditions. Quality control includes multiple rounds of annotation and expert review to ensure data consistency and accuracy. Image data is stored in JPG format, organized by category for easy subsequent processing and analysis.

Technical Specifications

Field Type Description
file_name string File name
quality string Resolution
crop_type string Identify the type of crop being sprayed in the image.
application_method string Identify how pesticides are being applied in the image, such as manual spraying or mechanical spraying.
daytime string Determine the time period when the image was taken, such as daytime or nighttime.
weather_condition string Identify the weather condition when the image was taken, such as sunny, cloudy, or rainy.
operator_presence boolean Determine if there is an operator present in the image.
equipment_type string Identify the type of spraying equipment used in the image.
target_area string Identify the area being sprayed in the image, such as leaves, stems, or blossoms.

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