Carrot Raw Material Recognition Dataset
#Object Detection #Image Classification #Crop Recognition #Agricultural Monitoring #Smart Agriculture
- 15000 records
- 3G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-12
AI Analysis & Value Prop
The current agricultural sector faces challenges in crop recognition and monitoring due to insufficient data and inaccurate labeling, which affects the development of smart agriculture. Existing solutions often rely on small-scale datasets, leading to poor model generalization capabilities. The Carrot Raw Material Recognition Dataset aims to provide a large-scale, high-quality annotated dataset to support object detection tasks in agriculture. The dataset ensures data diversity and representativeness by using drones and high-definition cameras to capture images of carrots in various environments. Additionally, multiple rounds of labeling and expert review quality control measures ensure high data quality and consistency. Data will be stored in JPG format, with corresponding JSON files provided, organized by category and scenario.
Dataset Insights
Sample Examples
c6a20dc6**.jpg|6960*4640|6.85 MB
331983cc**.jpg|4480*6720|3.11 MB
bf352cf0**.jpg|5392*3033|1.79 MB
07ae33ed**.jpg|7008*4672|6.11 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| carrot_type | string | Identifies the variety of the carrot, such as: regular carrot, purple carrot. |
| carrot_color | string | Identifies the color characteristics of the carrot, such as: orange, purple. |
| carrot_length | float | Identifies the length of the carrot in centimeters. |
| carrot_diameter | float | Identifies the diameter of the carrot in centimeters. |
| carrot_defect | string | Identifies defects on the surface of the carrot, such as: cracks, spots. |
| carrot_freshness | string | Identifies the freshness of the carrot, such as: fresh, not fresh. |
| carrot_leaves | integer | Identifies the number of leaves on the carrot. |
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 types of images are included in the Carrot Raw Material Identification Dataset?
- The Carrot Raw Material Identification Dataset includes images focused on carrot target detection in the agricultural field.
- How can the Carrot Raw Material Identification Dataset improve agricultural production?
- The Carrot Raw Material Identification Dataset can be used to enhance the recognition rate of carrots by automated equipment, thereby improving agricultural production efficiency.
- What are the application scenarios of the Carrot Raw Material Identification Dataset?
- The dataset can be applied in the development of smart agricultural equipment, automatic sorting robots, and improving the data analysis accuracy in agricultural IoT.
- How does the Carrot Raw Material Identification Dataset support machine learning model training?
- By providing a large amount of annotated image data, the dataset can be used to train and validate the performance of object detection models in carrot recognition tasks.
- Is the Carrot Raw Material Identification Dataset suitable for target detection research on other crops?
- Although the dataset focuses on carrots, its methodology and annotation techniques can provide references for target detection in other crops.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Carrot Raw Material Recognition Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/48c8608a9ccfbef04261d65b9d54da4b},
urldate={2025-09-15},
keywords={Carrot Recognition Dataset, Agricultural Object Detection Data, Smart Agriculture Dataset},
version={1.0}
}Using this in research? Please cite us.
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