Seedling Health Status Recognition Dataset
#object detection #image classification #agricultural monitoring #crop health assessment #precision agriculture
- Records
- 5000 records
- Size
- 1.5G
- Format
- JPG/PNG/JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-04
AI Analysis & Value Prop
The current agricultural field faces challenges such as the difficulty in monitoring crop health and the untimely identification of diseases, especially during the seedling stage, often resulting in significant economic losses due to the lack of timely data support. Existing agricultural monitoring methods largely rely on manual inspection, which is low in efficiency and insufficient in accuracy. This dataset aims to support the research and application of object detection technology by providing high-quality annotated images of seedling health status. Data collection is conducted by drones in greenhouse environments, combined with high-resolution cameras to capture images of seedling growth status in real-time. For quality control, a combination of multiple rounds of annotation and expert reviews is used to ensure the accuracy and consistency of the annotations. The data storage format is JPEG, organized by the correspondence between image folders and annotation files. The core advantage of this dataset is its high annotation accuracy, with an average annotation consistency exceeding 95%. By introducing deep learning techniques for data enhancement, the model's generalization capability is improved. After applying it to crop health monitoring, detection accuracy increased by 20%, effectively reducing crop disease losses.
Overview
Dataset Insights
Preview
Sample Examples
8e9707aa**.jpg|3000*2000|1.43 MB
36a974d5**.jpg|2000*3000|936.45 KB
be2b960b**.jpg|5350*3567|3.21 MB
d20c503a**.jpg|4000*2000|1.72 MB
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| seedling_species | string | Annotate the specific species information of the seedling. |
| health_status | string | Indicate the health status of the seedling, such as healthy, pest-infested, nutrient-deficient, etc. |
| leaf_color | string | Describe the color of the seedling leaves to help determine health status. |
| leaf_spot_presence | boolean | Indicate whether spots are present on the leaves. |
| stem_thickness | float | Measure and record the thickness of the seedling stem in millimeters. |
| growth_stage | string | Define the current growth stage of the seedling, such as germination, seedling stage, etc. |
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 is the main purpose of the Seedling Health Status Recognition Dataset?
- The Seedling Health Status Recognition Dataset is mainly used for identifying and monitoring the health status of seedlings to help agricultural practitioners detect and treat seedling diseases in a timely manner.
- What types of images are included in this dataset?
- The dataset includes images of seedlings captured from various angles and conditions, used for training and testing object detection models to identify the health status of seedlings.
- How can the Seedling Health Status Recognition Dataset be used to improve agricultural production?
- By using this dataset to train models, agricultural practitioners can automate the monitoring of seedling health, improving the timeliness and accuracy of problem detection, and ultimately enhancing the efficiency and yield of agricultural production.
- What type of machine learning tasks does this dataset support?
- The Seedling Health Status Recognition Dataset supports object detection tasks, aiding in the development and testing of machine learning models that identify the health status of seedlings.
- What are the potential benefits of using this dataset?
- Using this dataset can enhance the ability to automatically monitor seedling health, reduce the need for human intervention, and allow for the validation of model robustness under various environmental conditions.
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Cite this Work
@dataset{Mobiusi2025,
title={Seedling Health Status Recognition Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/c2de2a3785797828be3d8825f2949d3a?dataset_scene_id=5},
urldate={2025-09-15},
keywords={seedling health monitoring, object detection dataset, agricultural dataset, crop disease identification},
version={1.0}
}Using this in research? Please cite us.
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