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tags:
  - target detection
  - image classification
  - crop monitoring
  - pest and disease detection
  - precision agriculture
license: cc-by-nc-sa-4.0
task_categories:
  - object-detection
language:
  - en
pretty_name: Bean Leaf Detection Dataset
size_categories:
  - 1B<n<10B

Bean Leaf Detection Dataset

The current agricultural sector faces the challenge of crop pest and disease detection. Due to the lack of efficient and accurate detection technologies, crops suffer severe losses. Existing solutions often rely on manual detection, which is not only inefficient but also prone to errors. The Bean Leaf Detection Dataset aims to provide high-quality annotated image data to help researchers and developers develop more precise computer vision algorithms to automatically identify and detect pests and diseases on bean leaves. The data is collected by professionals in various field environments, using high-resolution cameras to ensure that image details are clearly distinguishable. Multiple rounds of annotation and consistency checks are implemented during data processing to ensure the accuracy and consistency of annotations. The data is stored in JPEG format, organized such that each image file corresponds to an annotation file, facilitating subsequent applications.

Technical Specifications

Field Type Description
file_name string File name
quality string Resolution
leaf_type string Identifies the type of bean leaf, such as soybean leaf or mung bean leaf.
disease_presence boolean Indicates whether there are any diseases or pests present on the leaf.
disease_type string Identifies the specific type of disease present on the leaf, such as powdery mildew or rust.
damage_severity string Evaluates the severity of the damage on the leaf, such as mild, moderate, or severe.
color_variation string Detects color changes on the leaf, such as yellowing or browning.
leaf_size string Measures the size of the leaf, such as large, medium, or small.
leaf_shape string Identifies the shape of the leaf, such as elliptical, heart-shaped, or needle-like.
texture string Identifies the texture characteristics of the leaf, such as smooth or rough.

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