Farmland Irrigation Scenario Classification Dataset

The current agricultural industry is facing challenges such as water resource shortages and low irrigation efficiency. Traditional manual monitoring methods are not only time-consuming and labor-intensive but also prone to human errors. Existing solutions often rely on basic sensors and monitoring systems, lacking intelligent and automated processing capabilities. This dataset aims to support the training of deep learning models by providing high-quality image data to improve the accuracy of irrigation scenario recognition and meet the needs of intelligent agriculture development. Data collection is conducted using drones, covering farmland under different climate and soil conditions to ensure data diversity. For quality control, multi-round annotation and expert review are adopted to ensure consistency and accuracy of annotation. The data storage format is JPEG, organized by folders for each scenario classification, facilitating data management and retrieval. This dataset features high annotation accuracy (>95%), consistency, and integrity. The annotation methods employ the latest computer vision technology combined with data augmentation techniques, significantly enhancing the robustness of models. Models applying this dataset have improved the accuracy of irrigation scenario recognition by 15%, effectively addressing the issue of low irrigation efficiency in agricultural production.