Orchard Vehicle Image Classification Dataset
#image classification #target recognition #agricultural automation #intelligent agriculture machinery #precision agriculture
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
The current agricultural sector faces challenges of labor shortages and low production efficiency, particularly in orchard management. Intelligent transport vehicles are gradually becoming an industry trend. However, existing vehicle recognition technologies often rely on traditional methods, which cannot effectively handle diverse vehicle types and complex environments. This dataset aims to provide a rich collection of image samples to address issues of accuracy and efficiency in vehicle recognition, thus supporting the development of agricultural automation. Data collection primarily uses high-resolution cameras in various orchard environments, including different weather conditions such as sunny and cloudy days. Multiple rounds of review and expert verification are conducted during the annotation process to ensure high-quality data. Data is stored in JPG format, with a clear structure, making it easy for subsequent processing and analysis.
Dataset Insights
Sample Examples
07933ec6**.jpg|1080*1439|366.27 KB
636ca9e2**.png|604*1019|1.18 MB
03c394ca**.jpg|1206*668|303.20 KB
788897d1**.jpg|1080*1439|335.18 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| vehicle_type | string | Identify the specific type of vehicle, such as tractor, truck, etc. |
| make | string | Identify the brand or manufacturer of the vehicle. |
| model | string | Identify the model of the vehicle. |
| color | string | Identify the color of the vehicle. |
| registration_plate | string | Identify the registration plate number of the vehicle. |
| loading_status | string | Identify whether the vehicle is fully loaded. |
| presence_of_farmer | boolean | Whether a farmer is present in the image. |
| weather_condition | string | Weather conditions at the time of capture, such as sunny, rainy, etc. |
| day_night | string | The time period when the picture was taken, such as day or night. |
| vehicle_orientation | string | Identify the orientation of the vehicle in the image. |
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 |
Frequently Asked Questions
- What vehicle types are included in the Orchard Vehicle Image Classification Dataset?
- The dataset typically includes various common orchard vehicle types, such as tractors, harvesters, and transport vehicles.
- How does the Orchard Vehicle Image Classification Dataset aid agricultural automation?
- By identifying different types of vehicles, this dataset can help agricultural systems manage vehicle operations more efficiently, enhancing automation.
- How is image quality ensured in the Orchard Vehicle Image Classification Dataset?
- The images in the dataset are typically rigorously selected and pre-processed to ensure their clarity and suitability for classification tasks.
- What are the potential challenges of using the Orchard Vehicle Image Classification Dataset?
- Potential challenges include identifying vehicles that are dispersed in images and handling images under varying lighting conditions.
- What technologies can be combined with the Orchard Vehicle Image Classification Dataset?
- This dataset can be combined with machine learning and deep learning technologies for developing automatic recognition systems.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Orchard Vehicle Image Classification Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/ec28f370af0212cbaa8c97ae8a1cab75?dataset_scene_id=5},
urldate={2025-09-15},
keywords={orchard vehicles, image classification dataset, agricultural automation, intelligent agriculture machinery, precision agriculture},
version={1.0}
}Using this in research? Please cite us.
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