Toward Autonomous Mobile Robot Navigation in Early-Stage Crop Growth

This paper presents a general procedure for enabling autonomous row following in crops during early-stage growth, without relying on absolute localization systems. A model based on deep learning techniques (object detection for wide-row crops and segmentation for narrow-row crops) was applied to accurately detect both types of crops. Tests were performed using a manually operated mobile platform equipped with an RGB and a time-of-flight (ToF) cameras. Data were acquired during different time periods and weather conditions, in maize and wheat fields. The results showed the success on crop detection and enables the future development of a fully autonomous navigation system in cultivated fields during early stage of crop growth.

Automated summary

This paper presents a general procedure for enabling autonomous row following in crops during early-stage growth. A model based on deep learning techniques accurately detects different types of crops, providing significant implications for the development of fully autonomous navigation systems during early-stage crop growth.