agriculture

Satellite image crop classification utilizes remote sensing technology for efficient monitoring and analysis of agricultural land. By acquiring satellite data at different times and spectral bands, the spectral characteristics of crops can be extracted to identify different crop types. In recent years, with the development of machine learning and deep learning algorithms, classification accuracy has significantly improved. By using vegetation indices (such as NDVI) and other spectral features, it is possible to effectively distinguish the changes in different crops throughout their growth stages. By analyzing the crop’s growth season and physiological status, and combining multi-temporal satellite images, accurate crop type identification can be achieved, providing data support for agricultural production and food security.