Farmland Fence Detection Dataset
#object detection #object recognition #farmland management #intelligent agriculture #drone monitoring
- Records
- 5000 records
- Size
- 1.5G
- Format
- JPG/PNG/JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-05
AI Analysis & Value Prop
The current agricultural industry faces challenges such as low efficiency in farmland management and difficulties in monitoring. Traditional manual inspection methods are not only time-consuming and labor-intensive but also prone to omissions. Existing monitoring solutions mostly rely on fixed cameras, which cannot adapt to the dynamic nature of farmland in real time, resulting in insufficient timeliness and accuracy of data. This dataset aims to help researchers and developers train efficient object detection models by providing a large number of farmland fence detection images, thereby enhancing the intelligence level of farmland monitoring. For data collection, drones are used to capture images of farmland fences under different times and weather conditions, ensuring diversity and comprehensiveness of the data. Quality control measures include multiple rounds of annotation and expert review to ensure consistency and accuracy of annotations. Data is stored with images in JPG format and annotations in JSON format, facilitating quick reading and processing.
Overview
Dataset Insights
Preview
Sample Examples
8e965556**.jpg|5184*3456|1.53 MB
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| fence_count | int | The total number of fences identified in the image. |
| fence_type | string | The type of fence, such as wooden fence, wire mesh, etc. |
| fence_material | string | The material used for the fence, such as wood, metal, etc. |
| fence_condition | string | The physical condition of the fence, such as intact, damaged, etc. |
| vegetation_coverage | float | The proportion of the area covered with vegetation in the image. |
| soil_type | string | The primary type of soil visible in the image. |
| weather_conditions | string | The weather conditions at the time the image was taken, such as sunny, cloudy, etc. |
| light_intensity | string | The intensity of light in the image, such as low, medium, high. |
| intrusion_status | string | Whether there are animals or humans intruding within the fenced area in the image. |
Trust
Compliance Statement
Authorization Type
Proprietary - Commercial AI Training License (No Redistribution)
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
FAQ
Frequently Asked Questions
- What can this dataset be used for?
- The farmland fence detection dataset can be used to develop and train object detection models in agriculture to achieve smart farmland management.
- What types of image information are included in the dataset?
- The dataset contains various images showing farmland fences from different perspectives and environments.
- Why is this dataset important for the agriculture sector?
- This dataset provides information on farmland boundaries, helping to improve the efficiency of agricultural resource management and promote precision agriculture.
- How can this dataset be used to enhance smart farmland management?
- By training object detection models, this dataset can be used to identify and monitor farmland boundaries, optimizing field planning and crop management.
- Will the application of this dataset affect productivity?
- Yes, using this dataset to develop object detection models can increase the automation of farmland management, thereby boosting productivity.
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Cite this Work
@dataset{Mobiusi2025,
title={Farmland Fence Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/3eb7912b1496d25ff8e2e74649ac4fa9?cate=2},
urldate={2025-10-23},
keywords={farmland fence detection, object detection dataset, agricultural intelligent monitoring, drone agriculture},
version={1.0}
}Using this in research? Please cite us.
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