Deep Learning Object Detection for Robotic Harvesters in Rice

In recent years, robotic combine harvesters have been developed globally to alleviate the shortage of agricultural labor. However, they are only allowed to work automatically under the supervision of a human operator, as they are unequipped with any object detection sensors for safety. To ensure a safe and efficient operation of a robotic combine harvester, a deep learning-based semantic segmentation method was applied for pixel-wise detection of various objects in paddy fields. The target objects to be detected by semantic segmentation included harvested rice areas, unharvested rice areas, lodging rice areas, humans, the header of the combine or field ridges. By using this technique, significant objects in the paddy field can be detected simultaneously, helping robotic combine harvester to identify their surrounding environment quickly and accurately.