Row Based Crop

Row-based crop automation focuses on developing autonomous robots for efficient and precise agricultural tasks within row crops like vineyards and flax fields. Current research emphasizes robust, low-cost navigation systems using computer vision (RGB-D cameras) and machine learning algorithms, including model predictive control and deep learning for tasks such as path planning, weed detection, and row following, even in GPS-denied environments. These advancements aim to improve efficiency, reduce herbicide use, and increase crop yields by enabling autonomous weed control, monitoring, and harvesting, ultimately impacting both agricultural practices and robotics research.