Crop Monitoring

Crop monitoring leverages advanced technologies to efficiently assess crop health, growth, and yield, ultimately aiming to optimize agricultural practices and resource management. Current research heavily utilizes machine learning, particularly deep learning architectures like YOLO variants and vision transformers, alongside remote sensing data from satellites and drones, and increasingly incorporates multimodal data fusion. This work is significant for improving precision agriculture, enabling timely interventions to mitigate crop losses, and supporting sustainable food production by enhancing resource efficiency and reducing environmental impact.