Durian Plantation Pest Borer Image Dataset
#Image Classification #Object Detection #Pest Recognition #Agricultural Pest Control #Crop Health Monitoring #Smart Agriculture Management
- 500 records
- 1.4G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-16
AI Analysis & Value Prop
This dataset features high-quality annotation precision and integrity, verified through multiple rounds of checks to ensure annotation consistency above 98% for each image. It utilizes the latest image enhancement technologies to improve recognition accuracy, combined with optimal deep learning-based algorithms for data processing. It excels in precise pest recognition, significantly improving control efficiency, increasing productivity by an average of 30% compared to traditional methods, and surpassing similar datasets by 20% in accuracy testing. Furthermore, the dataset includes pest images from multiple growth stages and environments, making it uniquely scarce in similar datasets and possessing wide application value, providing a foundation for expanding pest recognition applications to other crops.
Dataset Insights
Sample Examples
a12f9e89**.jpg|3024*4032|2.36 MB
27937f9b**.jpg|3024*4032|904.68 KB
c2fd4910**.jpg|3024*4032|1.93 MB
1abb4c82**.jpg|3024*4032|945.10 KB
cb02f08b**.jpg|2416*3393|923.42 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| insect_presence | boolean | Indicates whether insect pests are present in the image. |
| insect_count | integer | The number of insect pests detected in the image. |
| insect_type | string | The type of insect pests identified in the image. |
| plant_health_status | string | The health status of the plant as observed in the image. |
| damage_severity | string | The severity of damage caused by insect pests to the plant. |
| image_quality | string | The clarity and resolution quality of the image. |
| leaf_color | string | The color of the leaves as seen in the image. |
| leaf_spotting | boolean | Indicates whether there are spots on the leaves in the image. |
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 is the use of the durian planting pest identification borer image dataset?
- This dataset is used to identify and prevent borer pest infestations in durian planting, helping farmers take effective measures to protect durian crops.
- How many images does this dataset contain?
- The dataset contains hundreds of images, and the exact number may vary depending on the version.
- What are the characteristics of durian borer pests?
- Borer pests typically leave boreholes and tunnels on durian trees, which can be recognized through image identification.
- How does this dataset assist in the development of agricultural and forestry applications?
- By optimizing pest identification technology, it helps decrease crop damage and enhances the efficiency of pest monitoring and management.
- How to use this dataset for training a machine learning model?
- You can use the images in this dataset as training data for training and testing machine learning models to identify pests.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Durian Plantation Pest Borer Image Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/36a1058495bcc5b6260a704b0f145ba5},
urldate={2026-02-04},
keywords={Durian Pest Recognition, Agriforestry Dataset, Smart Agriculture, Pest Image Classification, Agricultural AI},
version={1.0}
}Using this in research? Please cite us.
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