Vegetable Leaf Segmentation Dataset
#image segmentation #target identification #crop monitoring #pest and disease identification #precision agriculture
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-09-20
AI Analysis & Value Prop
With the growth of the global population, agricultural production faces challenges between high efficiency and sustainability, especially in pest and disease control. Traditional methods are often inefficient and have negative impacts on the environment. Current solutions rely heavily on manual identification, which is inefficient and lacks accuracy. This dataset aims to provide high-quality vegetable leaf images and their segmentation masks to support the technical needs of automated identification and precision agriculture. Data collection uses high-resolution cameras in greenhouse environments, with images undergoing strict quality control, including expert review and multiple rounds of annotation to ensure consistency and accuracy. Data is stored in JPG format with a clear structure, facilitating subsequent processing and analysis.
Dataset Insights
Sample Examples
47c8a025**.jpg|3840*2560|2.32 MB
cefe68a1**.jpg|6000*4000|4.51 MB
23b75145**.jpg|6000*4000|1.05 MB
073e362d**.jpg|5350*3567|3.21 MB
ee1918ce**.jpg|2592*3888|850.01 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| leaf_type | string | Identify the type of vegetable leaf based on the image. |
| disease_type | string | If a disease is present, identify the type of disease. |
| color_information | string | Identify the main color information of the leaf. |
| texture_pattern | string | Analyze the surface texture pattern of the leaf. |
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 is the main purpose of the Vegetable Leaf Segmentation Dataset?
- The main purpose of the Vegetable Leaf Segmentation Dataset is to support precision agriculture in the agricultural sector. It uses semantic segmentation techniques to identify and classify different types of vegetable leaves, aiding farmers in better crop management.
- What contributions does this dataset make to agricultural research?
- This dataset provides a crucial foundation for agricultural research, facilitating the development of automated plant monitoring and management systems, which can increase yield and reduce labor costs.
- How can the Vegetable Leaf Segmentation Dataset enhance the efficiency of crop management?
- By training models using this dataset, machine learning algorithms can automatically recognize and distinguish between different vegetable leaves, helping to predict crop health and growth trends, thus enhancing crop management efficiency.
- Can the Vegetable Leaf Segmentation Dataset be used to train machine learning models?
- Yes, the Vegetable Leaf Segmentation Dataset provides training material for semantic segmentation models, helping improve their performance in identifying and segmenting vegetable leaf objects in images.
- What types of vegetable leaves are covered by this dataset?
- While specific types may vary, it generally includes leaves from common vegetable varieties such as lettuce, spinach, and cabbage.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Vegetable Leaf Segmentation Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/a0058d69c76e6441bc6cf148f70f0d49?dataset_scene_id=5},
urldate={2025-10-22},
keywords={vegetable leaf segmentation, semantic segmentation dataset, agricultural image processing, precision agriculture},
version={1.0}
}Using this in research? Please cite us.
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