Crop Flood Disaster Classification Dataset

The current agricultural industry faces challenges of frequent flood disasters, making it difficult to quickly assess crop damage, affecting the stability of agricultural production and supply chains. Existing solutions largely rely on manual assessments which are inefficient and highly subjective, failing to meet the need for rapid response. This dataset aims to help AI models quickly assess damage by providing images of crops with varying levels of flooding, improving the accuracy and efficiency of post-disaster assessments. Data collection is performed using a combination of high-altitude drone photography and ground sampling under different weather conditions and geographic environments. All data undergo multiple rounds of annotation and consistency checks to ensure accuracy and reliability of the labels, and are ultimately stored and organized in JPG format to facilitate subsequent machine learning and data analysis.