Manual Weed Removal Behavior Recognition Dataset
#object detection #behavior recognition #agricultural monitoring #smart agriculture #precision agriculture
- Records
- 15000 records
- Size
- 1.5G
- Format
- JPG/PNG/JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-05
AI Analysis & Value Prop
The current agricultural sector faces problems of low efficiency in weed removal and high labor costs. Traditional weed removal methods require a large amount of manpower, and existing smart weed removal technologies are still in the early stages, unable to effectively identify and handle different types of weeds. To address these issues, this dataset aims to provide a high-quality manual weed removal behavior recognition dataset to assist researchers and engineers in developing more efficient smart agricultural solutions. High-resolution cameras are used for data collection in real agricultural environments, ensuring the representativeness of the collected images. To ensure data quality, multiple rounds of annotation and expert reviews were conducted to ensure the consistency and accuracy of the annotations. The data is stored in JPG format and organized in folders for easy processing and analysis.
Overview
Dataset Insights
Preview
Sample Examples
bc027cd4**.jpg|6240*4160|7.47 MB
96853815**.jpg|4899*2514|2.24 MB
a4973c82**.jpg|4603*3049|2.04 MB
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| weed_type | string | Identify and label the type of weeds present in the images. |
| weed_location | string | Specify the location of weeds in the image, represented by coordinates. |
| crop_type | string | Identify and label the type of crops present in the images. |
| crop_health_status | string | Assess and label the health status of crops in the images. |
| weed_density | float | Calculate and label the density of weeds in a single image. |
| lighting_conditions | string | Describe the lighting conditions during image capture, such as sunny, cloudy, etc. |
| camera_angle | string | Record and label the camera angle used for taking the picture. |
| time_of_day | string | Label the specific time of day when the image was taken, such as morning or afternoon. |
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 Artificial Weeding Behavior Recognition Dataset?
- The Artificial Weeding Behavior Recognition Dataset is an object detection dataset focused on enhancing weeding efficiency through image recognition technology to support the development of smart agriculture.
- What agricultural applications can this dataset be used for?
- This dataset can be used to improve the recognition capabilities of automatic or semi-automatic weeding robots, thereby enhancing weeding efficiency in agricultural production.
- What types of images are included in the dataset?
- The dataset includes images from various shooting angles and under different lighting conditions meant for identifying weeds in farmland.
- How can this dataset enhance the efficiency of smart agriculture?
- By utilizing this dataset to train object detection models, more efficient weeding equipment can be developed, thereby reducing labor costs and increasing agricultural productivity.
- What are the advantages of the Artificial Weeding Behavior Recognition Dataset?
- This dataset offers diverse image data, supporting the development of more accurate and reliable weed recognition algorithms, thereby improving weeding accuracy and efficiency in practical applications.
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Cite this Work
@dataset{Mobiusi2025,
title={Manual Weed Removal Behavior Recognition Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/faa3b512212fa384c1f8e4c12260aa55?dataset_scene_id=5},
urldate={2025-09-15},
keywords={manual weeding, object detection dataset, agricultural monitoring, smart agriculture},
version={1.0}
}Using this in research? Please cite us.
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