Farmland Irrigation Demand Forecast Dataset

The current agricultural industry faces significant challenges in drought and water resource management, especially in arid regions where the effectiveness of farmland irrigation directly impacts crop yields. Existing irrigation decision systems often rely on experience and simple meteorological data, lacking real-time precise predictions, leading to under or over-irrigation and affecting farm productivity. This dataset aims to provide accurate AI forecasts for irrigation timing and water demand by integrating drought farmland imagery with environmental data, thereby enhancing the intelligence of irrigation management. Data collection involves drones capturing farmland images under varying environmental conditions, supplemented with real-time recording of temperature, humidity, and other data via environmental sensors. To ensure data quality, the data undergoes multiple rounds of labeling and consistency checks, reviewed by agricultural experts. Images are stored in JPG format, and environmental data is stored in JSON format, facilitating subsequent data processing and analysis. The core advantage of this dataset lies in its high labeling accuracy and consistency, with labeling error controlled within 5%. With the adoption of the latest image enhancement technologies, the generalization ability of models has improved, achieving a 15% increase in irrigation prediction accuracy. This dataset provides a practical solution for agricultural irrigation management, promoting the development of smart agriculture.