Datasets:
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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