Crop Waterlogging Condition Detection Dataset

The current agricultural sector faces challenges related to climate change and water resource management. The issue of crop waterlogging is becoming increasingly serious, affecting crop growth and yield. Existing monitoring methods largely rely on manual inspection, which is inefficient and prone to errors, unable to provide real-time feedback on crop status. This dataset aims to help AI systems quickly identify and predict crop waterlogging and hypoxia status through image data. Data is collected using drones and ground camera equipment in various fields and environmental conditions to ensure coverage of different growth stages and waterlogging scenarios. Regarding quality control, multiple rounds of annotation and expert review are conducted to ensure label consistency and accuracy. Data is stored in JPG format, organized by image ID, facilitating subsequent analysis and use. The core advantage of this dataset is its high annotation precision and consistency, with annotation accuracy exceeding 95%, significantly improving monitoring efficiency. Newly introduced image enhancement technology increases the model's robustness, allowing accurate waterlogging condition identification under various environmental conditions, helping farmers take timely actions to improve crop yield and address issues in practical production.