Crop Growth Anomaly Detection Dataset under Flooding Conditions
#object detection #anomaly recognition #crop monitoring #agricultural management #smart agriculture
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-04-16
AI Analysis & Value Prop
In the current agricultural environment, floods have severely impacted crop growth, causing anomalies such as wilting and lodging, resulting in significant losses for farmers. Existing monitoring methods largely rely on manual inspections, which are inefficient and prone to missing information. This dataset aims to provide anomaly detection data for crop growth under flooded conditions to support AI model training and application, enabling automated crop monitoring. Data collection uses drone photography technology, conducted in different flood environments to ensure diversity. In terms of quality control, the data undergoes multiple rounds of annotation and expert review to ensure consistency and accuracy. The data storage format is JPG, organized such that each image file corresponds to an annotation file for easy subsequent processing.
Dataset Insights
Sample Examples
7974f478**.png|1499*2000|3.85 MB
230a96cc**.png|1688*2000|3.98 MB
6de3b607**.png|1604*2000|2.94 MB
d0b915e5**.png|2092*2000|3.96 MB
c7b144d8**.png|3077*2000|6.67 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| crop_type | string | The specific type of crop, such as rice, corn, etc. |
| anomaly_type | string | Records the type of anomaly exhibited by the crops, such as yellowing of leaves, stem rot, etc. |
| anomaly_severity | string | Assess the severity of the crop anomaly, such as mild, moderate, severe. |
| flood_depth | float | The depth of flood water detected in the environment, measured in meters. |
| growth_stage | string | The current growth stage of the crop, such as seedling stage, booting stage. |
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 is the Crop Growth Anomaly Detection Dataset under Flood Conditions?
- The Crop Growth Anomaly Detection Dataset under Flood Conditions is an object detection dataset that records abnormal growth conditions of crops in flooded environments, primarily for AI model growth anomaly recognition.
- In what agricultural research can this dataset be applied?
- This dataset can be applied in studying the effects of flooding on crop growth, developing AI models for growth anomaly detection, and optimizing agricultural management and decision support systems.
- How to use this dataset to train AI models for growth anomaly detection?
- You can train AI object detection models using the images and annotated data from this dataset to learn to identify signs of abnormal crop growth under flooded conditions.
- What is the importance of object detection in agriculture?
- Object detection technology plays a crucial role in agriculture by automatically identifying and monitoring crop diseases, pest infestations, nutrient deficiencies, and growth abnormalities, thereby improving the efficiency and accuracy of crop management.
- How to evaluate the performance of AI models in growth anomaly detection?
- The performance of AI models in growth anomaly detection can be evaluated using metrics such as accuracy, recall, and F1 score, which reflect the model's capability in correctly identifying the location and classification of anomalies.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Crop Growth Anomaly Detection Dataset under Flooding Conditions},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/f5d774005468d8f508f737503c71f537?cate=2},
urldate={2025-09-15},
keywords={crop monitoring, anomaly detection, agricultural dataset, object detection, flood impact},
version={1.0}
}Using this in research? Please cite us.
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