Durian Planting Pest Identification - Yellow Peach Weevil Image Data
#Image classification #target detection #pattern recognition #Agricultural and forestry pest detection #durian planting management #agricultural intelligent monitoring
- 500 records
- 1.3G
- JPG
- CATL
MOBIUSI INC
Updated:2026-04-16
AI Analysis & Value Prop
Currently, durian planting faces significant losses due to pest infestations, particularly from yellow peach weevils. Traditional methods rely on manual pest monitoring, which is inefficient and costly. Existing automated pest identification solutions lack precision and adaptability to real-world environments. This dataset focuses on solving technical challenges in accurate pest identification, improving detection efficiency and accuracy. Data collection involves using high-resolution cameras in major durian production areas, with environments including natural and greenhouse lighting. Quality control includes multiple rounds of professional annotation and consistency review by a team of over 20 professionals with an agronomy background. Data preprocessing involves image enhancement and noise reduction, with data storage format in JPG, organized by region and pest type.
Dataset Insights
Sample Examples
c1fd5f94**.jpg|1920*2560|388.77 KB
3fa9afa1**.jpg|1920*2560|408.19 KB
90f0237c**.jpg|1920*2560|455.03 KB
cff41218**.jpg|1920*2560|284.15 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| insect_presence | boolean | Indicates whether the Gold Dust Weevil is present in the image. |
| insect_count | integer | The number of Gold Dust Weevils in the image. |
| damage_level | string | The level of damage caused by the Gold Dust Weevil to the durian plant, e.g., mild, moderate, severe. |
| plant_health | string | The health condition of the durian plant affected by pests, e.g., healthy, damaged, endangered. |
| image_clarity | string | Assessment of the image's clarity, e.g., clear, blurry. |
| lighting_condition | string | The lighting condition of the environment in which the image was captured, e.g., good, too dark, too bright. |
| image_angle | string | The angle at which the image was taken, e.g., top view, level view, bottom 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
- What pest detection algorithms can this dataset be used for research?
- This dataset can be used for research on pest detection with convolutional neural networks (CNN) and other deep learning algorithms.
- How many images are included in the dataset?
- The dataset includes multiple high-quality images of durian golden powder weevil pests.
- Does the dataset provide annotation information to support machine learning model training?
- Yes, the dataset includes annotation information to help support machine learning model training.
- For which crops is this dataset particularly useful?
- This dataset is particularly useful for durian cultivation, especially for detecting golden powder weevil pests.
- Can it be used for real-time pest detection application development?
- Yes, this dataset can be used for developing real-time pest detection applications.
- What other related research in the agriculture, forestry, and fisheries sector can this dataset be applied to?
- This dataset can also be applied to research in crop health monitoring and pesticide usage optimization.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Durian Planting Pest Identification - Yellow Peach Weevil Image Data},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/08955d4dc7bc5e81784ba50fd957d602?dataset_scene_cate_type=8},
urldate={2026-02-04},
keywords={durian pest identification, yellow peach weevil detection, agricultural intelligent monitoring},
version={1.0}
}Using this in research? Please cite us.
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