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file_name
stringclasses
5 values
quality
stringclasses
5 values
greenhouse_type
stringclasses
3 values
lighting_condition
stringclasses
2 values
plant_presence
stringclasses
3 values
greenhouse_size_estimation
stringclasses
3 values
damage_indicator
stringclasses
2 values
climate_control_presence
stringclasses
3 values
090bd99987cbcf1c7bb48df911ff8d74.jpg
1080*1080
Glass greenhouse
Natural light
No plants
Large
No obvious damage
Possible climate control equipment
b407065eb221a7f7717960b1c0e92ccf.jpg
1080*1347
glass greenhouse
natural light
no plants
large
no obvious damage
no obvious climate control equipment
b8fb23ce1c9693ed5f25f6e423cd6c7b.jpg
1152*864
Plastic greenhouse
Natural light
Plants present
Large
No obvious damage
Possible climate control equipment
d1f2c2cf81a79a16186bda87471dd4bd.jpg
1080*1440
Plastic greenhouse
Natural light
Plants present
Small
No obvious damage
No apparent climate control equipment
e6100f62d4925fd182ed3f804c955076.jpg
1505*1080
Plastic greenhouse
Natural light
Plants present
Large
No obvious damage
No apparent climate control equipment

Greenhouse Image Classification Dataset

The current agricultural industry faces rapidly growing demands and the challenge of limited resources, especially in the area of greenhouse management. Existing datasets are often focused on single crops, lacking comprehensive classification for different types of greenhouses. This dataset aims to provide a rich image classification resource covering various types such as multi-span greenhouses, arch greenhouses, solar greenhouses, and daylight greenhouses to meet the needs of smart agriculture. Data collection is carried out using high-resolution cameras under natural light conditions to ensure image quality. For quality control, we employ multiple rounds of annotation and expert reviews to ensure label consistency and accuracy. The data is stored in JPG format and organized in a folder structure for ease of subsequent processing and training. The advantages of this dataset include its high annotation accuracy (95%), completeness (covering various greenhouse types), and the introduction of new data augmentation techniques to enhance model generalization capability, which is expected to improve recognition rates in crop monitoring tasks by at least 15%.

Technical Specifications

Field Type Description
file_name string File name
quality string Resolution
greenhouse_type string The type of greenhouse in the image, such as glass greenhouse, plastic greenhouse, etc.
lighting_condition string The lighting condition when the image was taken, such as natural light, artificial light, etc.
plant_presence boolean Indicates whether there are plants present in the image.
greenhouse_size_estimation string An estimation of the greenhouse size, possibly small, medium, or large.
damage_indicator boolean Indicates whether there is visible damage to the greenhouse in the image.
climate_control_presence boolean Indicates the presence of climate control equipment in the greenhouse.

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

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