Dataset for Evaluating Weed Control by Agricultural Machinery
#object detection #image recognition #crop management #precision agriculture #smart agriculture
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-10
AI Analysis & Value Prop
The current agricultural industry faces numerous challenges in weed control efficiency, such as low efficiency and high costs of manual weeding. Existing automated weeding systems largely rely on traditional image processing technologies, which cannot effectively address complex field environments and diverse types of weeds. Therefore, this dataset aims to provide researchers with rich image data to enhance the accuracy and efficiency of automated weeding systems. The dataset includes images evaluating the effectiveness of weed control in different crop growth environments, ensuring coverage of various weed types and growth stages. Data collection is conducted using high-resolution cameras under different lighting and weather conditions to ensure data diversity and authenticity. Quality control measures include multiple rounds of annotation and expert review to ensure consistency and accuracy of annotations. The data is stored in JPG format and organized in a standardized structured manner.
Dataset Insights
Sample Examples
da68dd79**.jpg|6240*4160|7.47 MB
fafb6281**.jpg|4899*2514|2.24 MB
cd4f13bf**.jpg|4603*3049|2.04 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| weed_type | string | Type of weed identified in the image. |
| weed_count | int | Number of weeds identified in the image. |
| machinery_type | string | Type of machinery used. |
| machinery_id | string | Unique identifier for the machinery used. |
| weed_coverage | float | Percentage of the image area covered by weeds. |
| soil_condition | string | Condition of the soil surface shown in the image. |
| vegetation_type | string | Type of visible plants in the image excluding weeds. |
| lighting_condition | string | Lighting conditions during the image capture. |
| weather_condition | string | Weather conditions during the image capture. |
| image_quality | string | Assessment level of the image quality. |
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 information does the Agricultural Machine Weeding Effect Assessment Dataset contain?
- The dataset contains high-quality image information about agricultural machine weeding effects, suitable for object detection tasks.
- How to use the Agricultural Machine Weeding Effect Assessment Dataset for object detection?
- You can use the dataset's images to train models to detect vegetation and weeding effects in the images.
- Which research fields is the Agricultural Machine Weeding Effect Assessment Dataset suitable for?
- The dataset is primarily suitable for research in the agricultural field, especially studies related to agricultural machinery and weeding techniques.
- What machine learning tasks can be performed using this dataset?
- You can perform tasks such as object detection and image recognition using this dataset.
- What is the image quality of the Agricultural Machine Weeding Effect Assessment Dataset?
- The dataset provides high-quality images to support accurate object detection and recognition tasks.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Dataset for Evaluating Weed Control by Agricultural Machinery},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/0197312cf6fb0abb613cf2fae1aa698b},
urldate={2025-09-15},
keywords={agricultural machinery weed control, object detection dataset, agricultural image recognition, weed control evaluation},
version={1.0}
}Using this in research? Please cite us.
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