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kheireddinecharichi/Automatic-Farmland-Pest-Monitoring-Dataset

Automatic Farmland Pest Monitoring Dataset Current agriculture faces challenges in pest monitoring regarding real-time capability and accuracy. Traditional methods rely mostly on manual inspection, which is inefficient and prone to errors. Existing automated monitoring systems often fail to adapt to complex farmland environments, resulting in frequent occurrences of missed detections and false alarms. This dataset aims to provide high-quality data support for AI training by… See the full description on the dataset page: https://huggingface.co/datasets/kheireddinecharichi/Automatic-Farmland-Pest-Monitoring-Dataset.

sourceHugging Facecc-by-nc-sa-4.0updated 4mo agoView on Hugging Face

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Dataset Card

Automatic Farmland Pest Monitoring Dataset

Current agriculture faces challenges in pest monitoring regarding real-time capability and accuracy. Traditional methods rely mostly on manual inspection, which is inefficient and prone to errors. Existing automated monitoring systems often fail to adapt to complex farmland environments, resulting in frequent occurrences of missed detections and false alarms. This dataset aims to provide high-quality data support for AI training by combining individual pests and feeding traces to improve the accuracy and real-time capability of pest monitoring. Data is collected using drones and ground cameras, covering different types of farmland and climatic conditions. We conducted multiple rounds of annotation and ensured data quality through consistency checks and expert reviews. Data is stored in JPG images and JSON format annotation information, which is clearly structured for subsequent processing and analysis. This dataset has a high annotation accuracy with annotation consistency exceeding 90% and completeness over 95%. We introduce new data augmentation techniques to enhance the model's generalization ability, which is expected to increase pest recognition rates by 15% and reduce false alarm rates by 30%, offering significant application value.

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
insect_type	string	The type of insect pest identified in the image, such as locust or aphid.
damage_type	string	The type of plant damage identified in the image, such as leaf bite marks or stem damage.
damage_severity	string	The severity of the plant damage identified in the image, such as mild, moderate, or severe.
weather_condition	string	The weather condition at the time the image was taken, such as sunny, cloudy, or rainy.
plant_type	string	The type of plant identified that is being damaged by the insect pests, such as wheat or rice.

Compliance Statement

<table> <tr> <td>Authorization Type</td> <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td> </tr> <tr> <td>Commercial Use</td> <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td> </tr> <tr> <td>Privacy and Anonymization</td> <td>No PII, no real company names, simulated scenarios follow industry standards</td> </tr> <tr> <td>Compliance System</td> <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td> </tr> </table>

Source & Contact

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