Carrot Growth Stage Recognition Dataset
#object detection #image classification #agricultural monitoring #crop growth analysis #smart agriculture
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-10-24
AI Analysis & Value Prop
The current agricultural field faces challenges in crop growth monitoring, especially in precision agriculture, where timely identification and monitoring of crop growth stages is key to improving yield and quality. Existing solutions often rely on manual observation, which is inefficient and prone to errors. This dataset aims to solve the technical problems of automated recognition by providing high-quality images of carrot growth stages to meet the business needs of smart agriculture. The dataset contains 5,000 precisely annotated images of carrot growth stages, captured using professional cameras in various growing environments to ensure data diversity and accuracy. After data collection, multiple rounds of annotation and consistency checks were conducted to ensure the accuracy of each image's annotation, finally stored and organized in an efficient JPEG format.
Dataset Insights
Sample Examples
319a5733**.jpg|6960*4640|6.85 MB
95c0a34a**.jpg|4480*6720|3.11 MB
fc70b3b7**.jpg|7008*4672|6.11 MB
a7cb7289**.jpg|4272*2400|1.57 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| carrot_stage | string | Determine the growth stage of the carrot based on its appearance in the image. |
| carrot_height | float | Estimated height of the carrot displayed in the image, measured in centimeters. |
| leaf_color | string | The color of the carrot leaves, used to assess the health condition of growth. |
| soil_condition | string | The condition of the soil surface as identified from the image. |
| lighting_condition | string | Records the lighting condition at the time of image capture. |
| carrot_density | integer | The number of visible carrots in the image, which may affect their growth. |
| disease_signs | string | Detect possible disease signs displayed on the carrots in the image. |
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 |
Frequently Asked Questions
- What types of images are included in the Carrot Growth Stage Recognition Dataset?
- The dataset includes high-quality images of carrots at different growth stages, from germination to maturity.
- What are the applications of carrot growth stage recognition in agriculture?
- This dataset can be used to develop smart agriculture applications, helping farmers better monitor and manage carrot growth cycles.
- How can object detection techniques be used to recognize the growth stages of carrots?
- By using this dataset to train object detection models, one can automatically recognize carrot growth stages, enabling precision agriculture monitoring.
- How does the Carrot Growth Stage Recognition Dataset support smart agriculture development?
- The dataset provides training data for machine learning models to enhance automation in crop monitoring, improving agricultural production efficiency.
- What type of researchers or developers would find the Carrot Growth Stage Recognition Dataset suitable?
- This dataset is suitable for researchers and developers working in agri-tech, computer vision, and machine learning.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Carrot Growth Stage Recognition Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/3120624208d3d7f875e94f69a4e23af8},
urldate={2025-09-15},
keywords={carrot dataset, agricultural object detection, crop growth stage recognition},
version={1.0}
}Using this in research? Please cite us.
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