Crop Classification

Crop classification uses remote sensing data, primarily satellite imagery, to identify different crop types across agricultural lands. Current research focuses on improving classification accuracy and robustness using deep learning architectures like convolutional neural networks (CNNs), transformers, and hybrid models that combine their strengths, often incorporating techniques like multi-view learning and explainable AI (XAI) for enhanced interpretability. These advancements are crucial for optimizing agricultural practices, improving crop yield predictions, and supporting effective resource management, ultimately contributing to food security and sustainable agriculture.