Irrigation Machinery Recognition Dataset
#target detection #image classification #agricultural automation #irrigation management #smart agriculture
- 15000 records
- 3.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-07-27
AI Analysis & Value Prop
The current agricultural industry faces challenges such as water scarcity and climate change. Traditional irrigation methods are inefficient and fail to meet the demands of modern agriculture. Most existing agricultural machinery recognition systems rely heavily on manual annotation, resulting in low annotation efficiency and poor accuracy. Therefore, constructing a high-quality irrigation machinery recognition dataset aims to address the annotation accuracy and data diversity issues in target detection, to support the development of intelligent irrigation systems. The dataset construction includes images of irrigation machinery collected from different regions, using professional cameras under natural lighting to ensure data diversity and authenticity. In terms of quality control, we have implemented multiple rounds of annotation and consistency checks to ensure annotation accuracy and reliability. The data storage format is JPG, organized by category and shooting time, facilitating retrieval and analysis.
Dataset Insights
Sample Examples
d8d08f73**.jpg|3590*2003|2.35 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| object_type | string | Identifies the specific type of agricultural irrigation machinery in the image. |
| object_count | integer | Indicates the number of agricultural irrigation machines present in the image. |
| background_type | string | Describes the type of background in the image, such as field or farm. |
| weather_conditions | string | Describes the weather conditions at the time the image was taken, such as sunny or cloudy. |
| angle_of_view | string | Describes the angle of view when taking the image, such as side view or top view. |
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
- Why is the Agricultural Irrigation Machinery Recognition Dataset important?
- The Agricultural Irrigation Machinery Recognition Dataset assists machine learning models in recognizing and classifying irrigation equipment, enhancing agricultural productivity and water-saving efficiency.
- Which smart agriculture applications is this dataset suitable for?
- This dataset is suitable for precision agriculture management, water resource management systems, and automated monitoring of agricultural equipment.
- How can this dataset be used for training object detection models?
- This dataset can be used to train object detection models to recognize and locate agricultural irrigation machinery, enabling automation and optimization.
- What types of images does the Agricultural Irrigation Machinery Recognition Dataset include?
- The dataset includes images of various agricultural irrigation equipment from different angles and environmental conditions.
- What agricultural production improvements can this dataset bring?
- This dataset can improve irrigation efficiency, reduce labor demand, and optimize water management, thus enhancing sustainability in agricultural production.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Irrigation Machinery Recognition Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/54a783cec81539688fd8a8f078c64c18?cate=2},
urldate={2025-09-15},
keywords={agriculture dataset, irrigation machinery recognition, target detection dataset, smart agriculture},
version={1.0}
}Using this in research? Please cite us.
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