A state-of-the-art LiDAR-based navigation system enables autonomous navigation in challenging row-crop fields without global localization.
Large pre-trained models, known as foundation models (FMs), have the potential to revolutionize smart agriculture by offering versatile capabilities with minimal fine-tuning.
Introducing DAVIS-Ag, a dataset for active vision research in agriculture, facilitating prototyping and benchmarking.
Utilizing deep learning and pixel-based methods for precise crop segmentation in lavender fields using Sentinel-2 satellite imagery.
The author compares the performance of ChatGPT and Claude as potential agricultural extension agents in Pakistan, highlighting their abilities and limitations.