Mobiusi

Tea Garden Landscape Recognition Dataset

#image classification #object detection #plant identification #agricultural monitoring #environmental assessment
  • 5000 records
  • 1.2G
  • JPG/PNG/JSON
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-02-04

AI Analysis & Value Prop

In the current agricultural sector, managing and monitoring tea gardens faces many challenges, such as the impact of environmental changes on tea tree growth and the timely detection of pests and diseases. Existing solutions often rely on manual inspections, which are not only time-consuming but also prone to omissions. To improve the efficiency and accuracy of tea garden management, this dataset aims to help farmers quickly identify the health status and potential problems of tea gardens through image classification technology. The dataset is constructed by using high-resolution cameras to capture tea garden scenes in different climates and growth stages, and it undergoes multiple rounds of annotation and expert review to ensure consistency and accuracy. The data is stored in JPG format and organized in a folder structure for easy model training and testing.

Dataset Insights

Sample Examples

f28cd9ca**.jpg|5184*2912|2.70 MB

9ac14a09**.jpg|5397*3602|5.01 MB

Technical Specifications

FieldTypeDescription
file_namestringFile name
qualitystringResolution
tea_plant_presencebooleanDetermine whether there are tea plants present in the image.
tea_plant_densitystringIdentify the density of tea plants in the image by analyzing their distribution, such as sparse, moderate, or dense.
seasonstringIdentify the season when the image was captured based on characteristics of the plants and environment, such as spring, summer, autumn, or winter.
weather_conditionstringDetermine the weather condition in the image by analyzing the lighting and other environmental factors, such as sunny, cloudy, or rainy.
pathway_presencebooleanDetermine the presence of pathways for tea garden management in the image.
human_activitybooleanIdentify signs of human activity visible in the image.
equipment_presencebooleanIdentify the presence of equipment related to tea garden management in the image.

Compliance Statement

Authorization TypeCC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What is the Tea Garden Scenery Recognition Dataset?
The Tea Garden Scenery Recognition Dataset is an image dataset designed to improve tea garden management efficiency through image classification techniques.
What does the Tea Garden Scenery Recognition Dataset contain?
The Tea Garden Scenery Recognition Dataset primarily contains images related to tea gardens, used to train and test image classification models.
How can the Tea Garden Scenery Recognition Dataset improve agricultural efficiency?
By training models with the Tea Garden Scenery Recognition Dataset, various landscapes in tea gardens can be automatically recognized and classified, assisting in management and monitoring, thereby improving agricultural efficiency.
Which industries can benefit from the Tea Garden Scenery Recognition Dataset?
The agricultural sector, particularly industries involved in tea garden management and research, can benefit from the dataset by improving management efficiency and monitoring capabilities.
What is the role of the Tea Garden Scenery Recognition Dataset in image classification?
The Tea Garden Scenery Recognition Dataset is used to train image classification models to accurately differentiate and identify various tea garden landscapes.

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Cite this Work

@dataset{Mobiusi2025,
  title={Tea Garden Landscape Recognition Dataset},
  author={MOBIUSI INC},
  year={2025},
  url={https://www.mobiusi.com/datasets/99fd2e1f9c9bd0e6d30551d90823338c},
  urldate={2025-09-15},
  keywords={tea garden recognition, image classification dataset, agricultural dataset},
  version={1.0}
}

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