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Mobiusi/Green-Chili-Damage-Recognition-Dataset

Green Chili Damage Recognition Dataset The current agricultural industry faces challenges such as untimely crop pest and disease recognition and low accuracy, leading to serious crop losses. Existing recognition solutions often rely on manual experience, which is inefficient and prone to errors. This dataset aims to provide high-quality images of green chili damage to train deep learning models and improve the accuracy and efficiency of damage recognition. Data collection uses… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Green-Chili-Damage-Recognition-Dataset.

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

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Dataset Card

Green Chili Damage Recognition Dataset

The current agricultural industry faces challenges such as untimely crop pest and disease recognition and low accuracy, leading to serious crop losses. Existing recognition solutions often rely on manual experience, which is inefficient and prone to errors. This dataset aims to provide high-quality images of green chili damage to train deep learning models and improve the accuracy and efficiency of damage recognition. Data collection uses professional cameras under different lighting and environmental conditions to ensure diversity and representativeness. In terms of quality control, the data undergoes multiple rounds of annotation and consistency checks to ensure annotation accuracy. The data is stored in JPG format, organized by category, facilitating subsequent model training and testing.

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
object_type	string	The type of identified object, such as damaged area or normal area.
damage_severity	string	Classification of the severity of damage on the green pepper, such as mild, moderate, or severe.
image_quality	string	Assessment of the image quality, such as clear or blurry.
lighting_conditions	string	Description of the lighting conditions during image capture, such as bright or dim.

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