Crop Mapping

Crop mapping uses satellite imagery and other data to identify and classify different crop types across agricultural lands. Current research emphasizes improving accuracy and efficiency through advanced machine learning techniques, including deep learning architectures like Vision Transformers and U-Nets, often incorporating multi-modal data (e.g., combining optical and radar imagery, or satellite and weather data) and leveraging explainable AI to optimize feature selection. These advancements are crucial for precision agriculture, enabling better yield prediction, resource management, and informed decision-making in response to climate change and increasing food demands.